Access Restriction

Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Graph clustering ♦ Clustering algorithms ♦ Experimental evaluation ♦ Quality measures
Abstract A promising approach to graph clustering is based on the intuitive notion of intracluster density versus intercluster sparsity. As for the weighted case, clusters should accumulate lots of weight, in contrast to their connection to the remaining graph, which should be light. While both formalizations and algorithms focusing on particular aspects of this rather vague concept have been proposed, no conclusive argument on their appropriateness has been given. In order to deepen the understanding of particular concepts, including both quality assessment as well as designing new algorithms, we conducted an experimental evaluation of graph-clustering approaches. By combining proved techniques from graph partitioning and geometric clustering, we also introduce a new approach that compares favorably.
ISSN 10846654
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-06-12
Publisher Place New York
e-ISSN 10846654
Journal Journal of Experimental Algorithmics (JEA)
Volume Number 12
Page Count 26
Starting Page 1
Ending Page 26

Open content in new tab

   Open content in new tab
Source: ACM Digital Library