Mining frequent patterns without candidate generation

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 prolific 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 highly condensed, much smaller data structure, 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.

REFERENCES

• 1: R. Agarwal, C. Aggarwal, and V. V. V. Prasad. Depth-first generation of large itemsets for association rules. IBM Tech. Report RC21538, July 1999.
• 2: Ramesh C. Agarwal , Charu C. Aggarwal , V. V. V. Prasad, A tree projection algorithm for generation of frequent item sets, Journal of Parallel and Distributed Computing, v.61 n.3, p.350-371, March 1, 2001 [doi>10.1006/jpdc.2000.1693]
• 3: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 4: Rakesh Agrawal , Ramakrishnan Srikant, Mining Sequential Patterns, Proceedings of the Eleventh International Conference on Data Engineering, p.3-14, March 06-10, 1995
• 5: Roberto J. Bayardo, Jr., Efficiently mining long patterns from databases, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.85-93, June 01-04, 1998, Seattle, Washington, United States
• 6: Sergey Brin , Rajeev Motwani , Craig Silverstein, Beyond market baskets: generalizing association rules to correlations, Proceedings of the 1997 ACM SIGMOD international conference on Management of data, p.265-276, May 11-15, 1997, Tucson, Arizona, United States
• 7: Guozhu Dong , Jinyan Li, Efficient mining of emerging patterns: discovering trends and differences, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.43-52, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312191]
• 8: G. Grahne, L. Lakshmanan, and X. Wang. Efficient mining of constrained correlated sets. In ICDE'00.
• 9: Efficient Mining of Partial Periodic Patterns in Time Series Database, Proceedings of the 15th International Conference on Data Engineering, p.106, March 23-26, 1999
• 10: J. Han, J. Pei, and Y. Yin. Mining partial periodicity using frequent pattern trees. In GS Tech, Rep, 99-10, Simon Fraser University, July 1999.
• 11: M. Kamber, J. Han, and J. Y. Chiang. Metaruleguided mining of multi-dimensional association rules using data cubes. In KDD'97, pp. 207-210.
• 12: Mika Klemettinen , Heikki Mannila , Pirjo Ronkainen , Hannu Toivonen , A. Inkeri Verkamo, Finding interesting rules from large sets of discovered association rules, Proceedings of the third international conference on Information and knowledge management, p.401-407, November 29-December 02, 1994, Gaithersburg, Maryland, United States [doi>10.1145/191246.191314]
• 13: Brian Lent , Arun N. Swami , Jennifer Widom, Clustering Association Rules, Proceedings of the Thirteenth International Conference on Data Engineering, p.220-231, April 07-11, 1997
• 14: Heikki Mannila , Hannu Toivonen , A. Inkeri Verkamo, Discovery of Frequent Episodes in Event Sequences, Data Mining and Knowledge Discovery, v.1 n.3, p.259-289, 1997 [doi>10.1023/A:1009748302351]
• 15: Raymond T. Ng , Laks V. S. Lakshmanan , Jiawei Han , Alex Pang, Exploratory mining and pruning optimizations of constrained associations rules, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.13-24, June 01-04, 1998, Seattle, Washington, United States
• 16: Jong Soo Park , Ming-Syan Chen , Philip S. Yu, An effective hash-based algorithm for mining association rules, Proceedings of the 1995 ACM SIGMOD international conference on Management of data, p.175-186, May 22-25, 1995, San Jose, California, United States
• 17: Sunita Sarawagi , Shiby Thomas , Rakesh Agrawal, Integrating association rule mining with relational database systems: alternatives and implications, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, p.343-354, June 01-04, 1998, Seattle, Washington, United States
• 18: Ashoka Savasere , Edward Omiecinski , Shamkant B. Navathe, An Efficient Algorithm for Mining Association Rules in Large Databases, Proceedings of the 21th International Conference on Very Large Data Bases, p.432-444, September 11-15, 1995
• 19: Craig Silverstein , Sergey Brin , Rajeev Motwani , Jeffrey D. Ullman, Scalable Techniques for Mining Causal Structures, Proceedings of the 24rd International Conference on Very Large Data Bases, p.594-605, August 24-27, 1998
• 20: R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In KDD'97, pp. 67-73.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT

General Terms:
Design, Management, Theory