Our New Progress on Frequent/Sequential Pattern Mining We develop new frequent/sequential pattern mining methods Performance study on both synthetic and.

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Presentation transcript:

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

Mining Complete Set of Frequent Patterns on T10I4D100k

Mining Complete Set of Frequent Patterns on T25I20D100k

Mining Complete Set of Frequent Patterns on Connect-4

Mining Sequential Patterns on C10T4S16I4

Mining Sequential Patterns on C10T8S8I8

Scalability of Mining Sequential Patterns on C10-100T8S8I8

Scalability of Mining Sequential Patterns on C10-100T4S16I4

Why Prefix Is Faster Than GSP? Dataset C10T4S16I4Dataset C10T8S8I8

Mining Frequent Closed Itemsets on T25I20D100k

Mining Frequent Closed Itemsets on Connect-4

Mining Frequent Closed Itemsets on Pumsb

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), R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc Int. Conf. Very Large Data Bases, pages , Santiago, Chile, September 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 J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation, Proc. SIGMOD’2000, Dallas, TX, May 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 , Avignon, France, March N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In Proc. ICDT’99, Israel, January 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