Generalized Sequential Pattern Mining with Item Intervals

13 years 2 months ago
Generalized Sequential Pattern Mining with Item Intervals
Sequential pattern mining is an important data mining method with broad applications that can extract frequent sequences while maintaining their order. However, it is important to identify item intervals of sequential patterns extracted by sequential pattern mining. For example, a sequence < A, B > with a 1-day interval and a sequence < A, B > with a 1-year interval are completely different; the former sequence may have some association, while the latter may not. To adopt item intervals, two approaches have been proposed for integration of item intervals with sequential pattern mining; (1) constraint-based mining and (2) extended sequence-based mining. However, although constraint-based mining approach avoids the extraction of sequences with non-interest time intervals such as too long intervals it has setbacks in that it is difficult to specify optimal constraints related to item interval, and users must re-execute constraint-based algorithms with changing constraint value...
Yu Hirate, Hayato Yamana
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JCP
Authors Yu Hirate, Hayato Yamana
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