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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Mining High Utility Sequential Patterns Using Maximal Remaining Utility
verfasst von : Wei Song, Keke Rong
Erschienen in: Data Mining and Big Data
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Abstract
Mining high utility sequential pattern is an interesting problem in data mining. In this paper, we propose a new algorithm called high utility sequential pattern mining based on maximal remaining utility (HUSP-MRU). In HUSP-MRU, the maximal remaining utility (MRU) is defined as tighter upper bound of candidates. Representing the search space with lexicographic sequential pattern tree, the matrix structures are used for MRU storage, and branch as well as node pruning based on MRU are used for improving mining efficiency. Extensive tests conducted on publicly available datasets show that the proposed algorithm outperforms USpan algorithm in terms of mining efficiency.