Publication
Title
Mining cohesive itemsets in graphs
Author
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.
Language
English
Source (journal)
Lecture notes in computer science. - Berlin, 1973, currens
Source (book)
17th International Conference on Discovery Science (DS), OCT 08-10, 2014, Bled, SLOVENIA
Publication
Berlin : Springer-verlag berlin, 2014
ISBN
978-3-319-11812-3
978-3-319-11811-6
Volume/pages
8777(2014), p. 111-122
ISI
000360154800010
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 05.10.2015
Last edited 12.06.2017
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