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Verlagsausgabe
DOI: 10.5445/KSP/1000085951/01
Veröffentlicht am 10.12.2018

Symmetry-based Graph Clustering Partition Stability

Ball, Fabian; Geyer-Schulz, Andreas

Abstract:
Stability of clustering partitions (e. g. Gfeller et al., 2005; von Luxburg, 2010) and the problem of how to define good clusters (e. g. Hennig, 2015) are recurring topics in data analysis. We present a novel approach to characterize the stability of graph clustering partitions that is based solely on the graph’s automorphism group. All in all, three formally equivalent definitions are given, each from a different point of view. Two of these conditions are exploited for the design of an algorithm for fast stability detection of a graph clustering partition. These characterizations can be perfectly combined with others in terms of an additional constraint that a “good” clustering solution must fulfill. Our propositions are likely to be generalized for other data formats than graphs, provided the automorphism group is known.


Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
URN: urn:nbn:de:swb:90-883647
KITopen-ID: 1000088364
Erschienen in Archives of Data Science, Series A (Online First)
Band 4
Heft 1
Seiten 21 S. online
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