IncSpan: Incremental mining of sequential patterns in large database

Hong Cheng, Xifeng Yan, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many real life sequence databases grow incrementally. It is undesirable to mine sequential patterns from scratch each time when a small set of sequences grow, or when some new sequences are added into the database. Incremental algorithm should be developed for sequential pattern mining so that mining can be adapted to incremental database updates. However, it is nontrivial to mine sequential patterns incrementally, especially when the existing sequences grow incrementally because such growth may lead to the generation of many new patterns due to the interactions of the growing subsequences with the original ones. In this study, we develop an efficient algorithm, IncSpan, for incremental mining of sequential patterns, by exploring some interesting properties. Our performance study shows that IncSpan outperforms some previously proposed incremental algorithms as well as a non-incremental one with a wide margin.

Original languageEnglish (US)
Title of host publicationKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsR. Kohavi, J. Gehrke, W. DuMouchel, J. Ghosh
Pages527-532
Number of pages6
StatePublished - Dec 1 2004
EventKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Seattle, WA, United States
Duration: Aug 22 2004Aug 25 2004

Publication series

NameKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

OtherKDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CitySeattle, WA
Period8/22/048/25/04

Keywords

  • Buffering pattern
  • Incremental mining
  • Reverse pattern matching
  • Shared projection

ASJC Scopus subject areas

  • Engineering(all)

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