[en] In this paper, a Monte Carlo study on the performance of two–mode
cluster methods is presented. The synthetical data sets were generated to correspond
to two types of data consisting of overlapping as well as disjoint clusters.
Furthermore, the data sets differed in cluster number, degrees of within-group homogeneity
and between-group heterogeneity as well as degree of cluster overlap.
We found that the methods performed very differently depending on type of data,
number of clusters, homogeneity and cluster overlap.
Disciplines :
Social & behavioral sciences, psychology: Multidisciplinary, general & others
Identifiers :
UNILU:UL-CHAPTER-2011-132
Author, co-author :
Krolak-Schwerdt, Sabine ; University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Languages, Culture, Media and Identities (LCMI)
Wiedenbeck, Michael
Language :
English
Title :
The recovery performance of two-mode clustering methods: Monte Carlo experiment
Publication date :
2006
Main work title :
Studies in classification, data analysis and knowledge organization
Editor :
Spiliopoulou, M.
Kruse, R.
Borgelt, C.
Nürnberger, A.
Gaul, W.
Publisher :
Springer, Berlin, Unknown/unspecified
Collection name :
Vol. 31: From data and information analysis to knowledge engineering