Our New Progress on Frequent/Sequential Pattern Mining

We develop new frequent/sequential pattern mining methods

Performance study on both synthetic and real data sets shows that our methods outperform conventional ones in wide margins

Our newmethods

Conventionalmethods

Frequent patternmining

FP-growth Apriori, TreeProjection

Sequential patternmining

PrefixSpan,FreeSpan

GSP

Frequent closedpattern mining

CLOSET A-close, CHARM

Mining Complete Set of Frequent Patterns on T10I4D100k

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Apriori

TreeProjection

FP-growth

Mining Complete Set of Frequent Patterns on T25I20D100k

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0.00% 0.50% 1.00% 1.50%

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Apriori

TreeProjection

FP-growth

Mining Complete Set of Frequent Patterns on Connect-4

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70% 75% 80% 85% 90% 95%

Support threshold

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TreeProjection

FP-growth

Mining Sequential Patterns on C10T4S16I4

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0.00% 0.50% 1.00% 1.50% 2.00%

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PrefixSpan-1

PrefixSpan-2

GSP

FreeSpan-2

Mining Sequential Patterns on C10T8S8I8

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PrefixSpan-1

PrefixSpan-2

GSP

FreeSpan-2

Scalability of Mining Sequential Patterns on C10-100T8S8I8

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PrefixSpan-1

PrefixSpan-2

GSP

FreeSpan-2

Scalability of Mining Sequential Patterns on C10-100T4S16I4

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PrefixSpan-1

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GSP

FreeSpan-2

Why Prefix Is Faster Than GSP?

0.001

0.01

0.1

1

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100

0.00% 0.50% 1.00% 1.50% 2.00%

Support threshold

# cand/pattern inGSP

Runtime/proj. db inPrefixSpan

0.001

0.01

0.1

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100

0.00% 0.50% 1.00% 1.50% 2.00%

Support threshold

# cand/pattern inGSP

Runtime/proj. db inPrefixSpan

Dataset C10T4S16I4 Dataset C10T8S8I8

Mining Frequent Closed Itemsets on T25I20D100k

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0.7% 0.9% 1.1% 1.3% 1.5%

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A-CLOSE

CLOSET

ChARM

Mining Frequent Closed Itemsets on Connect-4

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40% 50% 60% 70% 80% 90% 100%

Support threshold

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CLOSET

ChARM

Mining Frequent Closed Itemsets on Pumsb

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75% 80% 85% 90% 95%

Support threshold

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CLOSET

ChARM

References R. Agarwal, C. Aggarwal, and V. V. V. Prasad. A tree projection algorithm for

generation of frequent itemsets. In Journal of Parallel and Distributed Computing (Special Issue on High Performance Data Mining), (to appear), 2000.

R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. 1994 Int. Conf. Very Large Data Bases, pages 487--499, Santiago, Chile, September 1994.

J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. Hsu. FreeSpan: Frequent pattern-projected sequential pattern mining. In Proc. KDD'2000, Boston, August 2000.

J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation, Proc. SIGMOD’2000, Dallas, TX, May 2000.

J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal, and M. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth, submitted for publication

R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proc. 5th Int. Conf. Extending Database Technology (EDBT), pages 3--17, Avignon, France, March 1996.

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In Proc. ICDT’99, Israel, January 1999.

M.J. Zaki and C. Hsiao. ChARM: An efficient algorithm for closed association rule mining. In Proc. KDD'2000, Boston, August 2000.

DBMiner Version 2.5 (Beta)

DBMiner Technology Inc.B.C. Canada

What we had for DBMiner 2.0…

Association module on data cubes Classification module on data cubes Clustering module on data cubes OLAP browser 3D Cube browser

What we will do in DBMiner 2.5…

Keep the existing association module and classification module in version 2.0

Change the existing clustering module Add new visual classification module

both on SQL server and OLAP Add new sequential pattern modules

on SQL server using FP algorithm

What we have done…

We have incorporated the existing association module and added OLAP browser Module

We have added the visual classification module

We have changed the existing clustering module

We have added the sequential pattern module

We are still in the development stage

Association module on data cubes

New sequential pattern module on SQL Server

New visual classification module on data cubes

New clustering module on data cubes