Multi-dimensional sequential pattern mining

  • Authors:

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

  • Affiliations:

  • Simon Fraser University, Burnaby, B.C., Canada;Simon Fraser University, Burnaby, B.C., Canada;Simon Fraser University, Burnaby, B.C., Canada;Simon Fraser University, Burnaby, B.C., Canada;Hewlett-Packard Labs., Palo Alto, CA;Hewlett-Packard Labs., Palo Alto, CA

  • Venue:
  • Proceedings of the tenth international conference on Information and knowledge management
  • Year:
  • 2001

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Abstract

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 multi-dimensional 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.