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Communication Dans Un Congrès Année : 2011

Efficient mining Top-k regular-frequent itemset using compressed tidsets

Résumé

Association rule discovery based on support-confidence frame-work is an important task in data mining. However, the occurrence frequency (support) of a pattern (itemset) may not be a sufficient criterion for discovering interesting patterns. Temporal regularity, which can be a trace of behavior, with frequency behavior can be revealed as an important key in several applications. A pattern can be regarded as a regular pattern if it occurs regularly in a user-given period. In this paper, we consider the problem of mining top-k regular-frequent itemsets from transactional databases without support threshold. A new concise representation, called compressed transaction-ids set (compressed tidset), and a single pass algorithm, called TR-CT (Top-k Regular frequent itemset mining based on Compressed Tidsets), are proposed to maintain occurrence information of patterns and discover k regular itemsets with highest supports, respectively. Experimental results show that the use of the compressed tidset representation achieves highly efficiency in terms of execution time and memory consumption, especially on dense datasets.
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Dates et versions

hal-00609549 , version 1 (19-07-2011)

Identifiants

  • HAL Id : hal-00609549 , version 1

Citer

Komate Amphawan, Philippe Lenca, Athasit Surarerks. Efficient mining Top-k regular-frequent itemset using compressed tidsets. PAKDD'11: Workshop on Behavior Informatics, May 2001, Shenzhen, China. pp.159 - 170. ⟨hal-00609549⟩
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