CSE5230 - Data Mining, 2002 Lecture 1.1
Data Mining - CSE5230
David Squire
Room 5.23A B Block, Caulfield
Ph. 9903 1033
(thanks to Robert Redpath for initial development of course resources)
CSE5230/DMS/2002/1
CSE5230 - Data Mining, 2002 Lecture 1.2
Lecture Outline
◆ Unit Outline◆ Definitions of Data Mining◆ A Case Study◆ The Process of Knowledge Discovery
❖ Data Selection❖ Data Preprocessing❖ Data Mining
◆ Data Mining Tasks◆ Data Mining Techniques◆ Data Mining & Data Warehousing, OLAP
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Course outline
◆ Objectives◆ Assessment◆ Lectures, the lecturer and consultation◆ Recommended reading◆ Unit web site
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Objectives
◆ To develop knowledge of techniques and methods for data mining in large databases, including both those currently being used and those which are presently being researched
◆ At the end of the unit the student should be able to
❖ describe the algorithms underlying the most common state-of-the-art data mining tools
❖ make an informed choice of data mining tool for a given problem.
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Assessment◆ Students will form groups of three or four. Each group
will prepare a research paper on a particular data mining technique and its applications.
◆ At the end of the semester, all the group papers will be bound together to form a book, where each paper will be a chapter. The production of this book is the aim of the whole class for the semester.
❖ Group Research paper 70%❖ Presentation of the paper 20%❖ Individual Literature survey 5%❖ Attendance at student paper presentations 5%
◆ See the Unit Outline handout for further details
CSE5230 - Data Mining, 2002 Lecture 1.6
Lectures
◆ The lectures will be held in lecture room S2.32 from 4:00 p.m. to 6:00 p.m. on Mondays.
◆ Notes for each week will be made available on the subject web page in PowerPoint and Postscript formats
❖ It is your responsibility to ensure that you have copies of all notes, including the assignments
CSE5230 - Data Mining, 2002 Lecture 1.7
Lecturer and Tutorials
◆ Lecturer:
David SquireRoom 5.23A Building B - Caulfield campusEmail: [email protected] Phone: 9903 1033
◆ Tutorials Times:❖ Monday 6:00pm to 8:00pm - B3.44, B3.46❖ Monday 8:00pm to 10:00pm - B3.48
(note: no tutorials in week 1)
CSE5230 - Data Mining, 2002 Lecture 1.8
Recommended Reading (1)◆ There is no prescribed text. Many books have
relevant chapters for the unit:❖ Berry J.A. & Linoff G.; Data Mining Techniques: For
Marketing, Sales, and Customer Support ; John Wiley & Sons, Inc.; 1997
❖ Cabena P., Hadjinian P., Stadler R., Verhees J., Zanasi A.; Discovering Data Mining: From Concept to Implementation; Prentice Hall PTR, 1998
❖ Fayyad U., Piatetsky-Shapiro G., Smyth P., and Uhurusamy R. (eds); Advances in Knowledge Discovery and Data Mining; AAAI Press, 1996
❖ Kennedy R.L., Lee Y., Van Roy B., Reed C.D., Lippman R.P.; Solving Data Mining Problems Through Pattern Recognition; Prentice Hall PTR, 1997
❖ Witten I. H. and Frank, E.; Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations; Morgan Kaufmann, 1999
CSE5230 - Data Mining, 2002 Lecture 1.9
Recommended Reading (2)
◆ You will also have to read extensively in journals and conference proceedings to prepare your research papers. Many links to these resources are provided at the unit web site:http://www.csse.monash.edu.au/courseware/cse5230/
◆ Information on the site will include:❖ Lectures (in Powerpoint and Postscript formats)❖ Links relevant to the subject❖ Other relevant documents and information
◆ You should check the unit web site each week
CSE5230 - Data Mining, 2002 Lecture 1.10
What is Data Mining?
Group Exercise
◆ Break into groups of 4 or 5 (i.e. your neighbours, don’t move around the room)
◆ Take 5 minutes to write down a definition of data mining - this can be in point form
◆ After 5 minutes, we will collect definitions from the class
CSE5230 - Data Mining, 2002 Lecture 1.11
Definitions of Data Mining (1)◆ Many Definitions
❖ “Data mining is an interdisciplinary field bringing togther techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large data bases”
Evangelos Simoudis in Cabena et al.❖ “Data mining is the extraction of implicit, previously
unknown, and potentially useful information from data”Witten & Frank
❖ “Data mining… is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules”
Berry & Linoff❖ “Data mining is a term usually applied to techniques that
can be used to find underlying structure and relationships in large amounts of data”
Kennedy et al.
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Definitions of Data Mining (2)◆ Use of analytical tools to discover knowledge
in a collection of data❖ The knowledge takes the form of patterns, relationships
and facts which would not otherwise be immediately apparent
◆ These analytical tools may be drawn from a number of disciplines, which include:
❖ machine learning❖ pattern recognition❖ statistics❖ artificial intelligence❖ human-computer interaction❖ information visualization❖ and many more...
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Data Mining
◆ Why has the area appeared?❖ Large volumes of data stored by organizations in a
competitive environment combined with advances in technologies which can be applied to the data
◆ Background and evolution❖ The failure of traditional approaches
◆ The need for Data Mining❖ Niche marketing, customer retention, the internet
◆ The means to implement Data Mining❖ The data warehouse, the available computing power,
effective modeling approaches
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A Case Study - Data Preparation(Cabena et al. page 106)
◆ Health Insurance Commission Australia❖ 550Gb online; 1300Gb in 5 year history DB❖ Aim to prevent fraud and inappropriate practice❖ Considered 6.8 million visits requesting up to 20
pathology tests and 17,000 doctors❖ Descriptive variables were added to the GP records❖ Records were pivoted to create separate records for each
pathology test❖ Records were then aggregated by provider number (GP)❖ An association discovery operation was carried out
CSE5230 - Data Mining, 2002 Lecture 1.15
An Association Rule
◆ The Rule❖ When a customer buys a shirt, in 70% of cases, he or she
will also buy a tie❖ The Confidence Factor is 70%
◆ The Support Factor❖ This occurs in 13.5% of all purchases❖ The Support Factor is 13.5%
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Case Study - Modeling and Analysis (1)
◆ Rules with a confidence factor greater than 50% were considered
◆ The software Intelligent Miner (IBM) was used ◆ The level of support was gradually reduced
❖ i.e. the number of records to which the rule applied was reduced
◆ Rules considered to be noise were excluded.◆ Domain knowledge indicated that some tests
should be excluded and more useful rules were revealed
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Case Study - Modeling and Analysis (2)◆ GP profiling was carried out◆ The new segments were related back to
existing classifications of GPs◆ Some rules corresponded to expensive tests
that could be substituted
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Episodes Database GP Database
Rules 1% supportIf test A then test B will occur in 62%
of cases
Segment 1 Segment 2 97 GPs 206 GPsScore = 1.8 Score = 2.7
Data Preparation Merge
Association Discovery Database Segmentation
CSE5230 - Data Mining, 2002 Lecture 1.19
Data Mining for Business Decision Support
(From Berry & Linoff 1997)
◆ Identify the business problem◆ Use data mining techniques to transform the
data into actionable information◆ Act on information◆ Measure the results
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The Process of Knowledge Discovery (1)
◆ Pre-processing❖ data selection❖ cleaning❖ coding
◆ Data Mining❖ select a model❖ apply the model
◆ Analysis of results and assimilation❖ Take action and measure the results
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The Process of Knowledge Discovery (2)
Data Cleaning & Enrichment
Coding Data mining Reportingselection
-domain consistency- clustering
- segmentation-de-duplication - prediction-disambiguation
Requirement Action
Feedback
Operational data External data
The Knowledge Discovery in Databases (KDD) process (Adriaans/Zantinge)
Information
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Data Selection
◆ Identify the relevant data, both internal and external to the organization
◆ Select the subset of the data appropriate for the particular data mining application
◆ Store the data in a database separate from the operational systems
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Data Preprocessing (1)
◆ Cleaning❖ Domain consistency: replace certain values with null❖ De-duplication: customers are often added to the DB on
each purchase transaction❖ Disambiguation: highlighting ambiguities for a decision
by the user» e.g. if names differed slightly but addresses were the
same
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Data Preprocessing (2)
◆ Enrichment❖ Additional fields are added to records from external
sources which may be vital in establishing relationships.
◆ Coding❖ e.g. take addresses and replace them with regional codes❖ e.g. transform birth dates into age ranges
◆ It is often necessary to convert continuous data into range data for categorization purposes.
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Data Mining
◆ Preliminary Analysis❖ Much interesting information can be found by querying
the data set❖ May be supported by a visualization of the data set.
◆ Choose a one or more modeling approaches◆ There are two styles of data mining
❖ Hypothesis testing❖ Knowledge discovery
◆ The styles and approaches are not mutually exclusive
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Data Mining Tasks◆ Various taxonomies exist. Berry & Linoff define 6
tasks:❖ Classification❖ Estimation❖ Prediction❖ Affinity Grouping❖ Clustering❖ Description
◆ The tasks are also referred to as operations. Cabena et al. define 4 operations:
❖ Predictive Modeling❖ Database Segmentation❖ Link Analysis❖ Deviation Detection
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Classification
◆ Classification involves considering the features of some object then assigning it it to some pre-defined class, for example:
❖ Spotting fraudulent insurance claims❖ Which phone numbers are fax numbers❖ Which customers are high-value
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Estimation
◆ Estimation deals with numerically valued outcomes rather than discrete categories as occurs in classification.
❖ Estimating the number of children in a family❖ Estimating family income
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Prediction
◆ Essentially the same as classification and estimation but involves future behaviour
◆ Historical data is used to build a model explaining behaviour (outputs) for known inputs
◆ The model developed is then applied to current inputs to predict future outputs
❖ Predict which customers will respond to a promotion❖ Classifying loan applications
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Affinity Grouping
◆ Affinity grouping is also referred to as Market Basket Analysis
◆ A common example is which items are bought together at the supermarket. Once this is known, decisions can be made on, for example:
❖ how to arrange items on the shelves❖ which items should be promoted together
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Clustering
◆ Clustering is also sometimes referred to as segmentation (though this has other meanings in other fields)
◆ In clustering there are no pre-defined classes. Self-similarity is used to group records. The user must attach meaning to the clusters formed
◆ Clustering often precedes some other data mining task, for example:
❖ once customers are separated into clusters, a promotion might be carried out based on market basket analysis of the resulting cluster
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Description
◆ A good description of data can provide understanding of behaviour
◆ The description of the behaviour can suggest an explanation for it as well
◆ Statistical measures can be useful in describing data, as can techniques that generate rules
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Deviation Detection
◆ Records whose attributes deviate from the norm by significant amounts are also called outliers
◆ Application areas include:❖ fraud detection❖ quality control❖ tracing defects.
◆ Visualization techniques and statistical techniques are useful in finding outliers
◆ A cluster which contains only a few records may in fact represent outliers
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Data Mining Techniques◆ Query tools◆ Decision Trees◆ Memory-Based Reasoning◆ Artificial Neural Networks◆ Genetic Algorithms◆ Association and sequence detection◆ Statistical Techniques◆ Visualization◆ Others (Logistic regression,Generalized
Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), K Means Clustering, ...)
CSE5230 - Data Mining, 2002 Lecture 1.35
Data Mining and the Data Warehouse
◆ Organizations realized that they had large amounts of data stored (especially of transactions) but it was not easily accessible
◆ The data warehouse provides a convenient data source for data mining. Some data cleaning has usually occurred. It exists independently of the operational systems
❖ Data is retrieved rather than updated❖ Indexed for efficient retrieval❖ Data will often cover 5 to 10 years
◆ A data warehouse is not a pre-requisite for data mining
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Data Mining and OLAP
◆ Online Analytic Processing (OLAP)◆ Tools that allow a powerful and efficient
representation of the data◆ Makes use of a representation known as a
cube◆ A cube can be sliced and diced◆ OLAP provide reporting with aggregation and
summary information but does not reveal patterns, which is the purpose of data mining
