document: Frequent regular itemset mining 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 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 .
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