Title
Mining cohesive itemsets in graphs Mining cohesive itemsets in graphs
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
conferenceObject
Publication
Berlin :Springer-verlag berlin ,
Subject
Engineering sciences. Technology
Computer. Automation
Source (journal)
DISCOVERY SCIENCE, DS 2014
Source (book)
17th International Conference on Discovery Science (DS), OCT 08-10, 2014, Bled, SLOVENIA
Volume/pages
8777(2014) , p. 111-122
ISSN
0302-9743
ISBN - Hoofdstuk
978-3-319-11812-3
ISI
000360154800010
ISBN - Hoofdstuk
978-3-319-11811-6
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Discovering patterns in graphs is a well-studied field of data mining. While a lot of work has already gone into finding structural patterns in graph datasets, we focus on relaxing the structural requirements in order to find items that often occur near each other in the input graph. By doing this, we significantly reduce the search space and simplify the output. We look for itemsets that are both frequent and cohesive, which enables us to use the anti-monotonicity property of the frequency measure to speed up our algorithm. We experimentally demonstrate that our method can handle larger and more complex datasets than the existing methods that either run out of memory or take too long.
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