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 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 language | English (US) |
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Pages | 81-88 |
Number of pages | 8 |
DOIs | |
State | Published - 2001 |
Externally published | Yes |
Event | Proceedings of the 2001 ACM CIKM: 10th International Conference on Information and Knowledge Management - Atlanta, GA, United States Duration: Nov 5 2001 → Nov 10 2001 |
Other
Other | Proceedings of the 2001 ACM CIKM: 10th International Conference on Information and Knowledge Management |
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Country/Territory | United States |
City | Atlanta, GA |
Period | 11/5/01 → 11/10/01 |
ASJC Scopus subject areas
- General Decision Sciences
- General Business, Management and Accounting