Multi-dimensional sequential pattern mining

Helen Pinto, Jiawei Han, Jian Pei, Ke Wang, Qiming Chen, Umeshwar Dayal

Research output: Contribution to conferencePaperpeer-review


Sequential pattern mining, which finds the set of frequent subsequences in sequence databases, is an important data-mining task and has broad applications. Usually, sequence patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region, time, customer group, and others. It is interesting and useful to mine sequential patterns associated with multi-dimensional information. In this paper, we propose the theme of multi-dimensional sequential pattern mining, which integrates the multidimensional analysis and sequential data mining. We also thoroughly explore efficient methods for multi-dimensional sequential pattern mining. We examine feasible combinations of efficient sequential pattern mining and multidimensional analysis methods, as well as develop uniform methods for high-performance mining. Extensive experiments show the advantages as well as limitations of these methods. Some recommendations on selecting proper method with respect to data set properties are drawn.

Original languageEnglish (US)
Number of pages8
StatePublished - 2001
Externally publishedYes
EventProceedings of the 2001 ACM CIKM: 10th International Conference on Information and Knowledge Management - Atlanta, GA, United States
Duration: Nov 5 2001Nov 10 2001


OtherProceedings of the 2001 ACM CIKM: 10th International Conference on Information and Knowledge Management
Country/TerritoryUnited States
CityAtlanta, GA

ASJC Scopus subject areas

  • General Decision Sciences
  • General Business, Management and Accounting


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