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...

• Type: Journal
• Year: 2006
• Where: JCP
• Authors: Yu Hirate, Hayato Yamana
• Keywords: JCP 2006, Mining, Sequential Pattern, Sequential Pattern Mining