Title
Deducing bounds on the support of itemsets Deducing bounds on the support of itemsets
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Subject
Computer. Automation
Source (journal)
Lecture notes in computer science
Volume/pages
2682(2004) , p. 214-233
ISSN
0302-9743
ISI
000225113300011
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
Abstract
Mining Frequent Itemsets is the core operation of many data mining algorithms. This operation however, is very data intensive and sometimes produces a prohibitively large output. In this paper we give a complete set of rules for deducing tight bounds on the support of an iternset if the supports of all its subsets are known. Based on the derived bounds [l, u] on the support of a candidate itemset I, we can decide not to access the database to count the support of I if I is larger than the support threshold (I will certainly be frequent), or if u is below the threshold (I will certainly fail the frequency test). We can also use the deduction rules to reduce the size of an adequate representation of the collection of frequent sets; all itemsets I with bounds [l, u], where l = u, do not need to be stored explicitly. To assess the usability in practice, we implemented the deduction rules and we present experiments on real-life data sets.
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