Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
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Journal of Intelligent Information Systems
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
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Frequent closed itemset based algorithms: a thorough structural and analytical survey
ACM SIGKDD Explorations Newsletter
Data Mining and Knowledge Discovery
Adequate condensed representations of patterns
Data Mining and Knowledge Discovery
Data & Knowledge Engineering
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A survey on condensed representations for frequent sets
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DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Interrelation analysis of celestial spectra data using constrained frequent pattern trees
Knowledge-Based Systems
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Expert Systems with Applications: An International Journal
Efficient frequent pattern mining based on Linear Prefix tree
Knowledge-Based Systems
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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.