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Our New Progress on Frequent/Sequential Pattern Mining

Our new methods for frequent/sequential pattern mining outperform conventional ones on synthetic and real data sets. Scalability and efficiency are significantly improved. Development includes association, classification, and clustering modules on data cubes.

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Our New Progress on Frequent/Sequential Pattern Mining

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  1. 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

  2. Mining Complete Set of Frequent Patterns on T10I4D100k

  3. Mining Complete Set of Frequent Patterns on T25I20D100k

  4. Mining Complete Set of Frequent Patterns on Connect-4

  5. Mining Sequential Patterns on C10T4S16I4

  6. Mining Sequential Patterns on C10T8S8I8

  7. Scalability of Mining Sequential Patterns on C10-100T8S8I8

  8. Scalability of Mining Sequential Patterns on C10-100T4S16I4

  9. Why Prefix Is Faster Than GSP? Dataset C10T4S16I4 Dataset C10T8S8I8

  10. Mining Frequent Closed Itemsets on T25I20D100k

  11. Mining Frequent Closed Itemsets on Connect-4

  12. Mining Frequent Closed Itemsets on Pumsb

  13. 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.

  14. DBMiner Version 2.5 (Beta) DBMiner Technology Inc. B.C. Canada

  15. 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

  16. 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

  17. 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

  18. Association module on data cubes

  19. New sequential pattern module on SQL Server

  20. New visual classification module on data cubes

  21. New clustering module on data cubes

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