8/2/2019 Deep_Visualization in Data Mining (2)

1/66

1

Visualization and Data Miningtechniques

By-Group number- 14

Chidroop Madhavarapu(105644921)

Deepanshu Sandhuria(105595184)Data Mining CSE 634

Prof. Anita Wasilewska

8/2/2019 Deep_Visualization in Data Mining (2)

2/66

2

References

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzd

http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%

http://www.geocities.com/anand_palm/

http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPap

http://www.cs.umn.edu/Research/shashi-group/

http://www.cs.umn.edu/Research/shashi-group/Book/sdb-chap1.pdf

http://www.cs.umn.edu/research/shashi-group/alan_planb.pdf http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual.pdfhttp://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%20Vis.pdfhttp://www.geocities.com/anand_palm/http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPaperTalkFilezSzits02.pdf/shekhar02cubeview.pdfhttp://www.cs.umn.edu/Research/shashi-group/http://www.cs.umn.edu/Research/shashi-group/Book/sdb-chap1.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://www.cs.umn.edu/Research/shashi-group/Book/sdb-chap1.pdfhttp://www.cs.umn.edu/Research/shashi-group/http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPaperTalkFilezSzits02.pdf/shekhar02cubeview.pdfhttp://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%20Vis.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

3/66

3

Motivation

Visualization for Data Mining

Huge amounts of information

Limited display capacity of output devices

Visual Data Mining (VDM) is a new approach forexploring very large data sets, combining traditionalmining methods and information visualization techniques.

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

4/66

4

Why Visual Data Mining

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

5/66

5

Why Visual Data Mining

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

6/66

6

VDM Approach

VDM takes advantage of both,

The power of automatic calculations, and The capabilities of human processing.

Human perception offers phenomenal abilities

to extract structures from pictures.

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

7/667

Levels of VDM

No or very limited integration Corresponds to the application of either traditional information

visualization or automated data mining methods.

Loose integration

Visualization and automated mining methods are appliedsequentially.

The result of one step can be used as input for another step.

Full integration Automated mining and visualization methods applied in parallel.

Combination of the results.

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

8/668

Methods of Data Visualization

Different methods are available for visualization of databased on type of data

Data can be

Univariate

Bivariate

Multivariate

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

9/669

Univariate data

Measurement of single quantitative variable

Characterize distribution

Represented using following methods

Histogram

Pie Chart

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

10/6610

Histogram

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

11/6611

Pie Chart

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

12/6612

Bivariate Data

Constitutes of paired samples of two quantitativevariables

Variables are related

Represented using following methods

Scatter plots

Line graphs

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

13/6613

Scatter plots

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

14/6614

Line graphs

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

15/6615

Multivariate Data

Multi dimensional representation of multivariatedata

Represented using following methods

Icon based methods

Pixel based methods

Dynamic parallel coordinate system

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

16/6616

Icon based Methods

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

17/6617

Pixel Based Methods

Approach:

Each attribute value is represented by one colored pixel(the value ranges of the attributes are mapped to a

fixed color map). The values of each attribute are presented in separate

sub windows.

Examples: Dense Pixel Displays

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

18/6618

Dense Pixel Display

Approach: Each attribute value is represented by one colored

pixel (the value ranges of the attributes are mapped toa fixed color map).

Different attributes are presented in separate sub

windows.

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

19/6619

Visual Data Mining: Framework andAlgorithm Development

Ganesh, M., Han, E.H., Kumar, V., Shekar, S., &Srivastava, J. (1996).

Working Paper. Twin Cities, MN: University of Minnesota,Twin Cities Campus.

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

20/6620

References

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzz

http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%2

http://www.geocities.com/anand_palm/

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual.pdfhttp://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%20Vis.pdfhttp://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.ailab.si/blaz/predavanja/ozp/gradivo/2002-Keim-Visualization%20in%20DM-IEEE%20Trans%20Vis.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/10335/ftp:zSzzSzftp.cs.umn.eduzSzdeptzSzuserszSzkumarzSzdatavis.pdf/ganesh96visual.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

21/6621

Abstract

VDM refers to refers to the use of visualization techniques in DataMining process to Evaluate Monitor Guide

This paper provides a framework for VDM via the loose coupling ofdatabases and visualization systems.

The paper applies VDM towards designing new algorithms that can

learn decision trees by manually refining some of the decisions madeby well known algorithms such as C4.5.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

22/66

22

Components of VQLBCI

The three major components of VQLBCI areVisualRepresentations, Computations and Events.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

23/66

23

Visual Development of Algorithms

Most interesting use of visual data mining is thedevelopment of new insights and algorithms.

The figure below shows the ER diagram for

learning classification decision trees.

This model allows the user to monitor the qualityand impact of decisions made by the learning

procedure.

Learning procedure can be refined interactively viaa visual interface.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

24/66

24

ER diagram for the search space of decision treelearning algorithm

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

25/66

25

General Framework

Learning a classification decision tree from a training dataset can be regarded as a process of searching for the bestdecision tree that meets user-provided goal constraints.

The problem space of this search process consists of ModelCandidates, Model Candidate Generator and ModelConstraints.

Many existing classification-learning algorithms like C4.5and CDP fit nicely within this search framework. Newlearning algorithms that fit users requirements can bedeveloped by defining the components of the problemspace.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

26/66

26

General Framework

Model Candidate corresponds to the partialclassification decision tree. Each node of thedecision tree is a Model Atom

Search process is the process of finding a final

model candidate such that it meets user goalspecifications. Model Candidate Generator transforms the current

model candidate into a new model candidate byselecting one model atom to expand from the

expandable leaf model atoms. Model Constraints (used by Model Candidate

Generator) provide controls and boundaries to thesearch space.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

27/66

27

Search Process

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

28/66

28

Acceptability Constraint

Model Constraints consist of Acceptability constraints,Expandability constraints and a Data-Entropy calculationfunction.

Acceptability constraint predicate specifies when a model

candidate is acceptable and thus allows search process tostop. EX: A1) Total no of expandable leaf model atoms = 0. A2) Overall error rate of the model candidate =

maximal allowable tree size.

A1 is used in C4.5 and CDP

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

29/66

29

Expandability Constraint

An Expandability constraint predicate specifieswhether a leaf model atom is expandable or not.EX: C4.5 uses E1 and E2 CDP uses E2 and E3

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

30/66

30

Traversal Strategy

Traversal strategy ranks expandable leaf modelatoms based on the model atom attributes. EX:

Increasing order of depth Decreasing order of depth Orders based on other model atom attributes.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

31/66

31

Steps in Visual Algorithm Development

No single algorithm is the best all the time,performance is highly data dependent.

By changing different predicates of model

constraints, users can construct new classification-learning algorithm.

This enables users to find an algorithm that works

the best on a given data set. Two algorithms are developed : BF based onBest First search idea and CDP+ which is amodification of CDP

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

32/66

32

BF

This algorithm is based on the Best-First searchidea. For Acceptability criteria, it includes A1 and A2

with a user specified acceptable error rate.

The Traversal strategy chosen is T3 In Best-First, expandable leaf model atoms are

ranked according to the decreasing order of thenumber of misclassified training cases. (local errorrate * size of subset training data set)

The traversal strategy will expand a model atomthat has the most misclassified training cases,thus reducing the overall error rate the most.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

33/66

33

CDP +

CDP+ is a modification of CDP

CDP has dynamic pruning using expandabilityconstraint E3.

Here, the depth is modified according to the sizeof the training data set of the model atom.

We set B is the branching factor of the decision tree, t is

the size of training data set belonging to modelatom, T is the whole training data set.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

34/66

34

Comparison of different classification learning algorithms

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

35/66

35

Experiment

The new BF and CDP+ algorithms are comparedwith the C4.5 and CDP algorithms. Various metrics are selected to compare the

efficiency, accuracy and size of final decision trees

of the classification algorithm. The generation efficiency of the nodes ismeasured in terms of the total number of nodesgenerated.

To compare accuracy of the various algorithms,the mean classification error on the test data setshave been computed.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

36/66

36

Classification error for 10 data sets

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

37/66

37

Nodes generated for 10 data sets

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

38/66

38

Final decision tree size

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

39/66

39

Results/Conclusion

CDP has accuracy comparable to C4.5 whilegenerating considerably fewer nodes.

CDP+ has accuracy comparable to C4.5 while

generating considerably fewer nodes. CDP+ outperformed CDP in error rate and number

of nodes generated. Considering all performance metrics together,

CDP+ is the best overall algorithm. Considering classification accuracy alone, C4.5P isthe winner.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

40/66

40

Conclusion

Different datasets require different algorithms forbest results. Diverse user requirements put different

constraints on the final decision tree.

The experiment shows that Interactive Visual DataMining Framework can help find the most suitablealgorithm for a given data set and group of userrequirements.

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

41/66

41

Data Mining for Selective Visualization ofLarge Spatial Datasets

Proceedings of 14th IEEE International Conference on Tools withArtificial Intelligence(ICTAI'02), 2002.

Washington (November 2002), DC, USA,

Shashi Shekhar, Chang-Tien Lu, Pusheng Zhang, Rulin Liu

Computer Science & Engineering DepartmentUniversity of Minnesota

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/

8/2/2019 Deep_Visualization in Data Mining (2)

42/66

42

References

http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.

http://www.cs.umn.edu/Research/shashi-group/

http://www.cs.umn.edu/Research/shashi-group/Book/sdb-chap1.pdf

http://www.cs.umn.edu/research/shashi-group/alan_planb.pdf http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/2763

http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://www.geocities.com/anand_palm/http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPaperTalkFilezSzits02.pdf/shekhar02cubeview.pdfhttp://www.cs.umn.edu/Research/shashi-group/http://www.cs.umn.edu/Research/shashi-group/Book/sdb-chap1.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://www.cs.umn.edu/research/shashi-group/alan_planb.pdfhttp://www.cs.umn.edu/Research/shashi-group/Book/sdb-chap1.pdfhttp://www.cs.umn.edu/Research/shashi-group/http://citeseer.ist.psu.edu/cache/papers/cs/27216/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EctluzSzPaperTalkFilezSzits02.pdf/shekhar02cubeview.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

43/66

43

Basic Terminology

Spatial databases Alphanumeric data + geographical cordinates

Spatial mining Mining of spatial databases

Spatial datawarehouse Contains geographical data

Spatial outliers Observations that appear to be inconsistent with the

remainder of that set of data

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

44/66

44

Spatial Cluster

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

45/66

45

Contribution

Propose and implement the CubeViewvisualization system General data cube operations Built on the concept of spatial data warehouse to

support data mining and data visualization Efficient and scalable spatial outlier detection

algorithms

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

46/66

46

Challenges in spatial data mining

Classical data mining - numbers and categories.Spatial data more complex and extended objects such as points, lines and polygons.

Second, classical data mining works with explicitinputs, whereas spatial predicates and attributesare often implicit.

Third, classical data mining treats each inputindependently of other inputs.

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

47/66

47

Application Domain

The Traffic Management Center - MinnesotaDepartment of Transportation (MNDOT) has adatabase to archive sensor network.

Sensor network includes about nine hundred stations each of which contains one to four loop detector

Measurement of Volume and occupancy. Volume is # vehicles passing through station in 5-minuteinterval

Occupancy is percentage of time station is occupied withvehicles

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

48/66

48

Basic Concepts

Spatial Data Warehouse Spatial Data Mining Spatial Outliers Detection

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

49/66

49

Spatial Data Warehouse

Employs data cube structure Outputs - albums of maps. Traffic data warehouse

Measures - volume and occupancy

Dimensions - time and space.

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

50/66

50

Spatial Data Mining

Process of discovering interesting and useful butimplicit spatial patterns. key goal is to partially automate knowledge

discovery

Search for nuggets of information embedded invery large quantities of spatial data.

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

51/66

51

Spatial Outliers Detection

Suspiciously deviating observations Local instability Each Station

Spatial attributes time, space

Non spatial attributes volume, occupancy

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

52/66

52

Basic Structure CubeView

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

53/66

53

CubeView Visualization System

Each node in cube a visualization style S - Traffic volume of station at all times. TTD Time of the day

TDW Day of the week STTD Daily traffic volume of each station TTD TDWS Traffic volume at each station at different

times on different days

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

54/66

54

Dimension Lattice

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

55/66

55

CubeView Visualization System

b l

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

56/66

56

CubeView Visualization System

C b i i li i S

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

57/66

57

CubeView Visualization System

Data Mining Algorithms for

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

58/66

58

Visualization Problem Definition

Given a spatial graph G ={ S , E } S - s1, s2, s3, s4..

E edges (neighborhood of stations) f ( x ) - attribute value for a data record N ( x )- fixed cardinality set of neighbors of x ) - Average attribute value of x neighbors S( x ) - difference of the attribute value of each data

object and the average attribute value of neighbors.

Data Mining Algorithms for

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

59/66

59

Visualization Problem Definition cont

S( x ) - difference of the attribute value of each dataobject and the average attribute value of neighbors.

Test for detecting an outlier

confidence level threshold

Data Mining Algorithms for

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdf

8/2/2019 Deep_Visualization in Data Mining (2)

60/66

60

Visualization

Few points

First, the neighborhood can be selected based on a fixedcardinality or a fixed graph distance or a fixed Euclidean distance.

Second, the choice of neighborhood aggregate function can bemean, variance, or auto-correlation.

Third, the choice for comparing a location with its neighbors canbe either just a number or a vector of attribute values.

Finally, the statistic for the base distribution can be selected asnormal distribution.

Data Mining Algorithms for

http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.pdfhttp://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/27637/http:zSzzSzwww-users.cs.umn.eduzSzzCz7EpushengzSzpubzSzkdd2001zSzkdd.pdf/shekhar01detecting.p