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- Title
Efficient mining of sequential patterns with time constraints by delimited pattern growth.
- Authors
Lin, Ming-Yen; Lee, Suh-Yin
- Abstract
An active research topic in data mining is the discovery of sequential patterns, which finds all frequent subsequences in a sequence database. The generalized sequential pattern (GSP) algorithm was proposed to solve the mining of sequential patterns with time constraints, such as time gaps and sliding time windows. Recent studies indicate that the pattern-growth methodology could speed up sequence mining. However, the capabilities to mine sequential patterns with time constraints were previously available only within the Apriori framework. Therefore, we propose theDELISP(delimited sequential pattern) approach to provide the capabilities within the pattern-growth methodology.DELISPfeatures in reducing the size of projected databases byboundedandwindowed projectiontechniques.Bounded projectionkeeps only time-gap valid subsequences andwindowed projectionsaves nonredundant subsequences satisfying the sliding time-window constraint. Furthermore, thedelimited growthtechnique directly generates constraint-satisfactory patterns and speeds up the pattern growing process. The comprehensive experiments conducted show thatDELISPhas good scalability and outperforms the well-knownGSPalgorithm in the discovery of sequential patterns with time constraints.
- Subjects
DATA mining; DATABASES; ALGORITHMS
- Publication
Knowledge & Information Systems, 2005, Vol 7, Issue 4, p499
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-004-0182-5