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Mining top-k regular-frequent itemsets using database partitioning and support estimation
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AI
DOI：10.1016/j.eswa.2011.08.055.png)
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摘要
En 中文
Temporal regularity of itemset appearance can be regarded as an important criterion for measuring the interestingness of itemsets in several applications. A frequent itemset can be said to be regular-frequent in a database if it appears at a regular period. Therefore, the problem of mining a complete set of regular-frequent itemsets requires the specification of a support and a regularity threshold. However, in practice, it is often difficult for users to provide an appropriate support threshold. In addition, the use of a support threshold tends to produce a large number of regular-frequent itemsets and it might be better to ask for the number of desired results. We thus propose an efficient algorithm for mining top-k regular-frequent itemsets without setting a support threshold. Based on database partitioning and support estimation techniques, the proposed algorithm also uses a best-first search strategy with only one database scan. We then compare our algorithm with the state-of-the-art algorithms for mining top-k regular-frequent itemsets. Our experimental studies on both synthetic and real data show that our proposal achieves high performance for small and large values of k. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Data mining
Association rule
Frequent itemset
Top-k itemset mining
Regular-frequent itemset
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IF:
7.5
论文数:
3.0W
被引数:
10.2W
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