Publication
Title
Efficient discovery of the most interesting associations
Author
Abstract
Self-sufficient itemsets have been proposed as an effective approach to summarizing the key associations in data. However, their computation appears highly demanding, as assessing whether an itemset is self-sufficient requires consideration of all pairwise partitions of the itemset into pairs of subsets as well as consideration of all supersets. This article presents the first published algorithm for efficiently discovering self-sufficient itemsets. This branch-and-bound algorithm deploys two powerful pruning mechanisms based on upper bounds on itemset value and statistical significance level. It demonstrates that finding top-k productive and nonredundant itemsets, with postprocessing to identify those that are not independently productive, can efficiently identify small sets of key associations. We present extensive evaluation of the strengths and limitations of the technique, including comparisons with alternative approaches to finding the most interesting associations.
Language
English
Source (journal)
ACM Transactions on knowledge discovery from data. - New York
Publication
New York : ACM, 2014
ISSN
1556-4681
Volume/pages
8:3(2014), 31 p.
Article Reference
15
ISI
000339111300005
Medium
E-only publicatie
Full text (Publishers DOI)
Full text (publishers version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 08.09.2014
Last edited 16.05.2017
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