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Journal Article
WIS: Weighted Interesting Sequential Pattern Mining with a Similar Level of Support and/or Weight
- Authors
- Un Il Yun
- Issue Date
- 2007-06
- Citation
- ETRI Journal, v.29, no.3, pp.336-352
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- Sequential pattern mining has become an essential task with broad applications. Most sequential pattern mining algorithms use a minimum support threshold to prune the combinatorial search space. This strategy provides basic pruning; however, it cannot mine correlated sequential patterns with similar support and/or weight levels. If the minimum support is low, many spurious patterns having items with different support levels are found; if the minimum support is high, meaningful sequential patterns with low support levels may be missed. We present a new algorithm, weighted interesting sequential (WIS) pattern mining based on a pattern growth method in which new measures, sequential s-confidence and w-confidence, are suggested. Using these measures, weighted interesting sequential patterns with similar levels of support and/or weight are mined. The WIS algorithm gives a balance between the measures of support and weight, and considers correlation between items within sequential patterns. A performance analysis shows that WIS is efficient and scalable in weighted sequential pattern mining.
- KSP Keywords
- Combinatorial Search, Growth method, Low support, Pattern growth, Performance analysis, Search Space, Sequential pattern mining, minimum support threshold, mining algorithm, new algorithm