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Pattern-Growth Methods

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Frequent Pattern Mining

Abstract

Mining frequent patterns has been a focused topic in data mining research in recent years, with the development of numerous interesting algorithms for mining association, correlation, causality, sequential patterns, partial periodicity, constraint-based frequent pattern mining, associative classification, emerging patterns, etc. Many studies adopt an Apriori-like, candidate generation-and-test approach. However, based on our analysis, candidate generation and test may still be expensive, especially when encountering long and numerous patterns.

A new methodology, called frequent pattern growth, which mines frequent patterns without candidate generation, has been developed. The method adopts a divide-and-conquer philosophy to project and partition databases based on the currently discovered frequent patterns and grow such patterns to longer ones in the projected databases. Moreover, efficient data structures have been developed for effective database compression and fast in-memory traversal. Such a methodology may eliminate or substantially reduce the number of candidate sets to be generated and also reduce the size of the database to be iteratively examined, and, therefore, lead to high performance.

In this paper, we provide an overview of this approach and examine its methodology and implications for mining several kinds of frequent patterns, including association, frequent closed itemsets, max-patterns, sequential patterns, and constraint-based mining of frequent patterns. We show that frequent pattern growth is efficient at mining large data-bases and its further development may lead to scalable mining of many other kinds of patterns as well.

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References

  1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. 1994 Int. Conf. Very Large Data Bases (VLDB'94), pages 487–499, Santiago, Chile, Sept. 1994.

    Google Scholar 

  2. R. Agrawal and R. Srikant. Mining sequential patterns. In Proc. 1995 Int. Conf. Data Engineering (ICDE'95), pages 3–14, Taipei, Taiwan, Mar. 1995.

    Google Scholar 

  3. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. 1993 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'93), pages 207–216, Washington, DC, May 1993.

    Google Scholar 

  4. R. Agarwal, C. C. Aggarwal, and V. V. V. Prasad. Depth-first generation of large itemsets for association rules. In IBM Technical Report RC21538, July 1999.

    Google Scholar 

  5. R. J. Bayardo. Efficiently mining long patterns from databases. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98), pages 85–93, Seattle, WA, June 1998.

    Google Scholar 

  6. R. J. Bayardo, R. Agrawal, and D. Gunopulos. Constraint-based rule mining on large, dense data sets. In Proc. 1999 Int. Conf. Data Engineering (ICDE'99), pages 188–197, Sydney, Australia, April 1999.

    Google Scholar 

  7. K. Beyer and R. Ramakrishnan. Bottom-up computation of sparse and iceberg cubes. In Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'99), pages 359–370, Philadelphia, PA, June 1999.

    Google Scholar 

  8. S. Brin, R. Motwani, and C. Silverstein. Beyond market basket: Generalizing association rules to correlations. In Proc. 1997 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'97), pages 265–276, Tucson, AZ, May 1997.

    Google Scholar 

  9. C. Chen, X. Yan, F. Zhu, and J. Han. gApprox: Mining frequent approximate patterns from a massive network. In Proc. 2007 Int. Conf. Data Mining (ICDM'07), Omaha, NE, Oct. 2007.

    Google Scholar 

  10. H. Cheng, X. Yan, J. Han, and C.-W. Hsu. Discriminative frequent pattern analysis for effective classification. In Proc. 2007 Int. Conf. Data Engineering (ICDE'07), pages 716–725, Istanbul, Turkey, April 2007.

    Google Scholar 

  11. H. Cheng, X. Yan, J. Han, and P. S. Yu. Direct discriminative pattern mining for effective classification. In Proc. 2008 Int. Conf. Data Engineering (ICDE'08), Cancun, Mexico, April 2008.

    Google Scholar 

  12. G. Dong and J. Li. Efficient mining of emerging patterns: Discovering trends and differences. In Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD'99), pages 43–52, San Diego, CA, Aug. 1999.

    Google Scholar 

  13. M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, and J. D. Ullman. Computing iceberg queries efficiently. In Proc. 1998 Int. Conf. Very Large Data Bases (VLDB'98), pages 299–310, New York, NY, Aug. 1998.

    Google Scholar 

  14. G. Grahne and J. Zhu. Efficiently using prefix-trees in mining frequent itemsets. In Proc. ICDM'03 Int. Workshop on Frequent Itemset Mining Implementations (FIMI'03), Melbourne, FL, Nov. 2003.

    Google Scholar 

  15. G. Grahne, L.V.S. Lakshmanan, and X. Wang. Efficient mining of constrained correlated sets. In Proc. 2000 Int. Conf. Data Engineering (ICDE'00), pages 512–521, San Diego, CA, Feb. 2000.

    Google Scholar 

  16. J. Han, G. Dong, and Y. Yin. Efficient mining of partial periodic patterns in time series database. In Proc. 1999 Int. Conf. Data Engineering (ICDE'99), pages 106–115, Sydney, Australia, April 1999.

    Google Scholar 

  17. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M.-C. Hsu. FreeSpan: Frequent pattern-projected sequential pattern mining. In Proc. 2000 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD'00), pages 355–359, Boston, MA, Aug. 2000.

    Google Scholar 

  18. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proc. 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'00), pages 1–12, Dallas, TX, May 2000.

    Google Scholar 

  19. F. Korn, A. Labrinidis, Y. Kotidis, and C. Faloutsos. Ratio rules: A new paradigm for fast, quantifiable data mining. In Proc. 1998 Int. Conf. Very Large Data Bases (VLDB'98), pages 582–593, New York, NY, Aug. 1998.

    Google Scholar 

  20. L.V.S. Lakshmanan, R. Ng, J. Han, and A. Pang. Optimization of constrained frequent set queries with 2-variable constraints. In Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'99), pages 157–168, Philadelphia, PA, June 1999.

    Google Scholar 

  21. W. Li, J. Han, and J. Pei. CMAR: Accurate and efficient classification based on multiple class-association rules. In Proc. 2001 Int. Conf. Data Mining (ICDM'01), pages 369–376, San Jose, CA, Nov. 2001.

    Google Scholar 

  22. B. Liu, W. Hsu, and Y. Ma. Integrating classification and association rule mining. In Proc. 1998 Int. Conf. Knowledge Discovery and Data Mining (KDD'98), pages 80–86, New York, NY, Aug. 1998.

    Google Scholar 

  23. H. Mannila, H. Toivonen, and A. I. Verkamo. Efficient algorithms for discovering association rules. In Proc. AAAI'94 Workshop on Knowledge Discovery in Databases (KDD'94), pages 181–192, Seattle, WA, July 1994.

    Google Scholar 

  24. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1:259–289, 1997.

    Article  Google Scholar 

  25. R. Ng, L.V.S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'98), pages 13–24, Seattle, WA, June 1998.

    Google Scholar 

  26. B. Özden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. In Proc. 1998 Int. Conf. Data Engineering (ICDE'98), pages 412–421, Orlando, FL, Feb. 1998.

    Google Scholar 

  27. J. Pei and J. Han. Can we push more constraints into frequent pattern mining? In Proc. 2000 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD'00), pages 350–354, Boston, MA, Aug. 2000.

    Google Scholar 

  28. J. Pei, J. Han, and R. Mao. CLOSET: An efficient algorithm for mining frequent closed itemsets. In Proc. 2000 ACM-SIGMOD Int. Workshop on Data Mining and Knowledge Discovery (DMKD'00), pages 11–20, Dallas, TX, May 2000.

    Google Scholar 

  29. J. Pei, J. Han, and L.V.S. Lakshmanan. Mining frequent itemsets with convertible constraints. In Proc. 2001 Int. Conf. Data Engineering (ICDE'01), pages 433–442, Heidelberg, Germany, April 2001.

    Google Scholar 

  30. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu. PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth. In Proc. 2001 Int. Conf. Data Engineering (ICDE'01), pages 215–224, Heidelberg, Germany, April 2001.

    Google Scholar 

  31. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang. H-Mine: Hyper-structure mining of frequent patterns in large databases. In Proc. 2001 Int. Conf. Data Mining (ICDM'01), pages 441–448, San Jose, CA, Nov. 2001.

    Google Scholar 

  32. J. Pei, X. Zhang, M. Cho, H. Wang, and P. S. Yu. Maple: A fast algorithm for maximal pattern-based clustering. In Proceedings of the Third IEEE International Conference on Data Mining (ICDM'03), Melbourne, Florida, Nov. 2003. IEEE.

    Google Scholar 

  33. H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen, and U. Dayal. Multi-dimensional sequential pattern mining. In Proc. 2001 Int. Conf. Information and Knowledge Management (CIKM'01), pages 81–88, Atlanta, GA, Nov. 2001.

    Google Scholar 

  34. C. Silverstein, S. Brin, R. Motwani, and J. D. Ullman. Scalable techniques for mining causal structures. In Proc. 1998 Int. Conf. Very Large Data Bases (VLDB'98), pages 594–605, New York, NY, Aug. 1998.

    Google Scholar 

  35. R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. In Proc. 5th Int. Conf. Extending Database Technology (EDBT'96), pages 3–17, Avignon, France, Mar. 1996.

    Google Scholar 

  36. R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In Proc. 1997 Int. Conf. Knowledge Discovery and Data Mining (KDD'97), pages 67–73, Newport Beach, CA, Aug. 1997.

    Google Scholar 

  37. X. Yan and J. Han. gSpan: Graph-based substructure pattern mining. In Proc. 2002 Int. Conf. Data Mining (ICDM'02), pages 721–724, Maebashi, Japan, Dec. 2002.

    Google Scholar 

  38. X. Yan and J. Han. CloseGraph: Mining closed frequent graph patterns. In Proc. 2003 ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD'03), pages 286–295, Washington, DC, Aug. 2003.

    Google Scholar 

  39. F. Zhu, X. Yan, J. Han, P. S. Yu, and H. Cheng. Mining colossal frequent patterns by core pattern fusion. In Proc. 2007 Int. Conf. Data Engineering (ICDE'07), Istanbul, Turkey, April 2007.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, 61801, Urbana, IL, USA

    Jiawei Han

  2. Simon Fraser University, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

Authors
  1. Jiawei Han
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  2. Jian Pei
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Correspondence to Jiawei Han .

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Editors and Affiliations

  1. IBM, Yorktown Heights, New York, USA

    Charu C. Aggarwal

  2. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Jiawei Han

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Han, J., Pei, J. (2014). Pattern-Growth Methods. In: Aggarwal, C., Han, J. (eds) Frequent Pattern Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-07821-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-07821-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07820-5

  • Online ISBN: 978-3-319-07821-2

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