Data Mining and Knowledge Discovery

, Volume 8, Issue 1, pp 53–87 | Cite as

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

  • Jiawei Han
  • Jian Pei
  • Yiwen Yin
  • Runying Mao
Article

Abstract

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.

In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.

frequent pattern mining association mining algorithm performance improvements data structure 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agarwal, R., Aggarwal, C., and Prasad, V.V.V. 2001. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing, 61:350–371Google Scholar
  2. Agrawal, R., Imielinski, T., and Swami, A. 1993. Mining association rules between sets of items in large databases. In Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'93), Washington, DC, pp. 207–216.Google Scholar
  3. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A.I. 1996. Fast discovery of association rules. In Advances in Knowledge Discovery and Data Mining, U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (Eds.), AAAI/MIT Press, pp. 307–328.Google Scholar
  4. Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proc. 1994 Int. Conf. Very Large Data Bases (VLDB'94), Santiago, Chile, pp. 487–499.Google Scholar
  5. Agrawal, R. and Srikant, R. 1995. Mining sequential patterns. In Proc. 1995 Int. Conf. Data Engineering (ICDE'95), Taipei, Taiwan, pp. 3–14.Google Scholar
  6. Bayardo, R.J. 1998. Efficiently mining long patterns from databases. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98), Seattle, WA, pp. 85–93.Google Scholar
  7. Brin, S., Motwani, R., and Silverstein, C. 1997. Beyond market basket: Generalizing association rules to correlations. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'97), Tucson, Arizona, pp. 265–276.Google Scholar
  8. Dong, G. and Li, J. 1999. Efficient mining of emerging patterns: Discovering trends and differences. In Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD'99), San Diego, CA, pp. 43–52.Google Scholar
  9. Grahne, G., Lakshmanan, L., and Wang, X. 2000. Efficient mining of constrained correlated sets. In Proc. 2000 Int. Conf. Data Engineering (ICDE'00), San Diego, CA, pp. 512–521.Google Scholar
  10. Han, J., Dong, G., and Yin, Y. 1999. Efficient mining of partial periodic patterns in time series database. In Proc. 1999 Int. Conf. Data Engineering (ICDE'99), Sydney, Australia, pp. 106–115.Google Scholar
  11. Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. In Proc. 2000 ACMSIGMOD Int. Conf. Management of Data (SIGMOD'00), Dallas, TX, pp. 1–12.Google Scholar
  12. Kamber, M., Han, J., and Chiang, J.Y. 1997. Metarule-guided mining of multi-dimensional association rules using data cubes. In Proc. 1997 Int. Conf. Knowledge Discovery and Data Mining (KDD'97), Newport Beach, CA, pp. 207–210.Google Scholar
  13. Lent, B., Swami, A., and Widom, J. 1997. Clustering association rules. In Proc. 1997 Int. Conf. Data Engineering (ICDE'97), Birmingham, England, pp. 220–231.Google Scholar
  14. Mannila, H., Toivonen, H., and Verkamo, A.I. 1994. Efficient algorithms for discovering association rules. In Proc. AAAI'94 Workshop Knowledge Discovery in Databases (KDD'94), Seattle, WA, pp. 181–192.Google Scholar
  15. Mannila, H., Toivonen, H., and Verkamo, A.I. 1997. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1:259–289.Google Scholar
  16. Ng, R., Lakshmanan, L.V.S., Han, J., and Pang, A. 1998. Exploratory mining and pruning optimizations of constrained associations rules. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98), Seattle, WA, pp. 13–24.Google Scholar
  17. Pasquier, N., Bastide, Y., Taouil, R., and Lakhal, L. 1999. Discovering frequent closed itemsets for association rules. In Proc. 7th Int. Conf. Database Theory (ICDT'99), Jerusalem, Israel, pp. 398–416.Google Scholar
  18. Park, J.S., Chen, M.S., and Yu, P.S. 1995. An effective hash-based algorithm for mining association rules. In Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'95), San Jose, CA, pp. 175–186.Google Scholar
  19. Pei, J., Han, J., and Lakshmanan, L.V.S. 2001. Mining frequent itemsets with convertible constraints. In Proc. 2001 Int. Conf. Data Engineering (ICDE'01), Heidelberg, Germany, pp. 433–332.Google Scholar
  20. Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., and Yang, D. 2001. H-Mine: Hyper-structure mining of frequent patterns in large databases. In Proc. 2001 Int. Conf. Data Mining (ICDM'01), San Jose, CA, pp. 441–448.Google Scholar
  21. Pei, J., Han, J., and Mao, R. 2000. CLOSET: An efficient algorithm for mining frequent closed itemsets. In Proc. 2000 ACM-SIGMOD Int. Workshop Data Mining and Knowledge Discovery (DMKD'00), Dallas, TX, pp. 11–20.Google Scholar
  22. Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., and Hsu, M.-C. 2001. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proc. 2001 Int. Conf. Data Engineering (ICDE'01), Heidelberg, Germany, pp. 215–224.Google Scholar
  23. Srikant, R. and Agrawal, R. 1996. Mining sequential patterns: Generalizations and performance improvements. In Proc. 5th Int. Conf. Extending Database Technology (EDBT'96), Avignon, France, pp. 3–17.Google Scholar
  24. Silverstein, C., Brin, S., Motwani, R., and Ullman, J. 1998. Scalable techniques for mining causal structures. In Proc. 1998 Int. Conf. Very Large Data Bases (VLDB'98), New York, NY, pp. 594–605.Google Scholar
  25. Savasere, A., Omiecinski, E., and Navathe, S. 1995. An efficient algorithm for mining association rules in large databases. In Proc. 1995 Int. Conf. Very Large Data Bases (VLDB'95), Zurich, Switzerland, pp. 432–443.Google Scholar
  26. Sarawagi, S., Thomas, S., and Agrawal, R. 1998. Integrating association rule mining with relational database systems: Alternatives and implications. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD '98), Seattle, WA, pp. 343–354.Google Scholar
  27. Srikant, R., Vu, Q., and Agrawal, R. 1997. Mining association rules with item constraints. In Proc. 1997 Int. Conf. Knowledge Discovery and Data Mining (KDD'97), Newport Beach, CA, pp. 67–73.Google Scholar
  28. Zaki, M.J. and Hsiao, C.J. 2002. CHARM: An efficient algorithm for closed itemset mining. In Proc. 2002 SIAM Int. Conf. Data Mining, Arlington, VA, pp. 457–473.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Jiawei Han
    • 1
  • Jian Pei
    • 2
  • Yiwen Yin
    • 3
  • Runying Mao
    • 4
  1. 1.University of Illinois at Urbana-ChampaignUSA
  2. 2.State University of New York at BuffaloUSA
  3. 3.Simon Fraser UniversityUSA
  4. 4.Microsoft CorporationUSA

Personalised recommendations