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ABSTRACT

We introduce a new algorithm for mining sequential patterns. Our algorithm is especially efficient when the sequential patterns in the database are very long. We introduce a novel depth-first search strategy that integrates a depth-first traversal of the search space with effective pruning mechanisms.Our implementation of the search strategy combines a vertical bitmap representation of the database with efficient support counting. A salient feature of our algorithm is that it incrementally outputs new frequent itemsets in an online fashion.In a thorough experimental evaluation of our algorithm on standard benchmark data from the literature, our algorithm outperforms previous work up to an order of magnitude.

REFERENCES

• 1: 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
• 2: Rakesh Agrawal , Ramakrishnan Srikant, Mining Sequential Patterns, Proceedings of the Eleventh International Conference on Data Engineering, p.3-14, March 06-10, 1995
• 3: 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
• 4: C. Bettini, X. S. Wang, and S. Jajodia. Mining temporal relationships with multiple granularities in time sequences. Data Engineering Bulletin, 21(1):32--38, 1998.
• 5: Douglas Burdick , Manuel Calimlim , Johannes Gehrke, MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases, Proceedings of the 17th International Conference on Data Engineering, p.443-452, April 02-06, 2001
• 6: Minos N. Garofalakis , Rajeev Rastogi , Kyuseok Shim, SPIRIT: Sequential Pattern Mining with Regular Expression Constraints, Proceedings of the 25th International Conference on Very Large Data Bases, p.223-234, September 07-10, 1999
• 7: 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
• 8: H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering frequent episodes in sequences. In KDD 1995, pages 210--215, Montreal, Quebec, Canada, 1995.
• 9: Jian Pei , Jiawei Han , Behzad Mortazavi-Asl , Helen Pinto , Qiming Chen , Umeshwar Dayal , Meichun Hsu, PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth, Proceedings of the 17th International Conference on Data Engineering, p.215-224, April 02-06, 2001
• 10: Ramakrishnan Srikant , Rakesh Agrawal, Mining Sequential Patterns: Generalizations and Performance Improvements, Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, p.3-17, March 25-29, 1996
• 11: Ramakrishnan Srikant , Rakesh Agrawal, Mining Sequential Patterns: Generalizations and Performance Improvements, Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, p.3-17, March 25-29, 1996
• 12: Mohammed J. Zaki, SPADE: An Efficient Algorithm for Mining Frequent Sequences, Machine Learning, v.42 n.1-2, p.31-60, January-February 2001 [doi>10.1023/A:1007652502315]

INDEX TERMS

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

Additional Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.2 Nonnumerical Algorithms and Problems
          Subjects: Pattern matching

  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.3 Picture/Image Generation
          Subjects: Bitmap and framebuffer operations
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Pattern analysis

General Terms:
Algorithms, Experimentation, Measurement, Performance