Publication
Title
Mining association rules in graphs based on frequent cohesive itemsets
Author
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.
Language
English
Source (journal)
Lecture notes in computer science. - Berlin, 1973, currens
Publication
Berlin : 2015
ISSN
0302-9743 [print]
1611-3349 [online]
Volume/pages
9078(2015), p. 637-648
ISI
000361909900050
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 08.04.2016
Last edited 09.06.2017
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