Title
Mining association rules in graphs based on frequent cohesive itemsets Mining association rules in graphs based on frequent cohesive itemsets
Author
Faculty/Department
Faculty of Sciences. Biology
Faculty of Sciences. Chemistry
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
[*]
Subject
Computer. Automation
ISSN
0302-9743
ISI
000361909900050
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Searching for patterns in graphs is an active field of data mining. In this context, most work has gone into discovering subgraph patterns, where the task is to find strictly defined frequently re-occurring structures, i.e., node labels always interconnected in the same way. Recently, efforts have been made to relax these strict demands, and to simply look for node labels that frequently occur near each other. In this setting, we propose to mine association rules between such node labels, thus discovering additional information about correlations and interactions between node labels. We present an algorithm that discovers rules that allow us to claim that if a set of labels is encountered in a graph, there is a high probability that some other set of labels can be found nearby. Experiments confirm that our algorithm efficiently finds valuable rules that existing methods fail to discover.
E-info
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