Reference : A Clustering Approach using Weighted Similarity Majority Margins
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/285
A Clustering Approach using Weighted Similarity Majority Margins
English
Bisdorff, Raymond mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Meyer, Patrick [Télécom Bretagne, France]
Olteanu, Alexandru mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
2011
Advanced Data Mining and Applications ADMA, Beijing spring 2013
Springer-Verlag
LNAI 7120
15-28
Yes
No
International
978-3-642-25853-4
ADMA 2011
17-19 December 2011
Beijing
China
[en] Multiple Criteria Clustering ; Bipolar-valued Similarity ; Meta-heuristic Algorithm
[en] We propose a meta-heuristic for clustering objects that are described on multiple incommensurable attributes of nominal, ordinal and/or cardinal type. Our approach makes use of an innovative bipolar-valued dual similarity-dissimilarity relation characterized by pairwise weighted majority margins of similar minus dissimilar attribute evaluations. The clustering is computed in two steps. First, an evolutionary algorithm searches for a suitable subset of maximal similarity cliques that will best serve as cluster cores. In a second step, we construct with a greedy heuristic, around these initial cluster cores, a corresponding final partition which best fits the given bipolar-valued similarity relation.
http://hdl.handle.net/10993/285
10.1007/978-3-642-25853-4_2
LNAI 7120
Part I
Advanced Data Mining and Applications ADMA 201 J. Tang et al. (Eds)

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