Frequent regular itemset mining

Ruggieri S.

Closed and Free Itemsets  Data sets  Data analysts  Concise Representations  Frequent Itemsets 

Concise representations of frequent itemsets sacrifice readability and direct interpretability by a data analyst of the concise patterns extracted. In this paper, we introduce an extension of itemsets, called regular, with an immediate semantics and interpretability, and a conciseness comparable to closed itemsets. Regular itemsets allow for specifying that an item may or may not be present; that any subset of an itemset may be present; and that any non-empty subset of an itemset may be present. We devise a procedure, called {\bf RegularMine}, for mining a set of regular itemsets that is a concise representation of frequent itemsets. The procedure computes a covering, in terms of regular itemsets, of the frequent itemsets in the class of equivalence of a closed one. We report experimental results on several standard dense and sparse datasets that validate the proposed approach.

Source: 16th ACM International Conference on Knowledge Discovery and Data Mining (KDD 2010), pp. 263–272, Washington D.C., USA, 25-28 July 2010

Publisher: ACM Press, New York, USA

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