Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/6945
Title: Non-derivable itemset mining
Authors: Calders, Toon
GOETHALS, Bart 
Issue Date: 2007
Publisher: SPRINGER
Source: Data mining and knowledge discovery, 14(1). p. 171-206
Abstract: All frequent itemset mining algorithms rely heavily on the monotonicity principle for pruning. This principle allows for excluding candidate itemsets from the expensive counting phase. In this paper, we present sound and complete deduction rules to derive bounds on the support of an itemset. Based on these deduction rules, we construct a condensed representation of all frequent itemsets, by removing those itemsets for which the support can be derived, resulting in the so called Non-Derivable Itemsets (NDI) representation. We also present connections between our proposal and recent other proposals for condensed representations of frequent itemsets. Experiments on real-life datasets show the effectiveness of the NDI representation, making the search for frequent non-derivable itemsets a useful and tractable alternative to mining all frequent itemsets
Document URI: http://hdl.handle.net/1942/6945
ISSN: 1384-5810
e-ISSN: 1573-756X
DOI: 10.1007/s10618-006-0054-6
ISI #: 000244483000006
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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