Publication
Title
Deducing bounds on the support of itemsets
Author
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.
Language
English
Source (journal)
Lecture notes in computer science. - Berlin, 1973, currens
Publication
Berlin : 2004
ISSN
0302-9743 [print]
1611-3349 [online]
Volume/pages
2682(2004), p. 214-233
ISI
000225113300011
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 08.10.2008
Last edited 21.08.2017
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