Reference : A Clustering Approach using Weighted Similarity Majority Margins

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/285

Title :	A Clustering Approach using Weighted Similarity Majority Margins
Language :	English
Author, co-author :	Bisdorff, Raymond [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Meyer, Patrick [Télécom Bretagne, France]
	Olteanu, Alexandru [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2011
Main document title :	Advanced Data Mining and Applications ADMA, Beijing spring 2013
Publisher :	Springer-Verlag
Collection and collection volume :	LNAI 7120
Pages :	15-28
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-3-642-25853-4
Event name :	ADMA 2011
Event date :	17-19 December 2011
Event place (city) :	Beijing
Event country :	China
Keywords :	[en] Multiple Criteria Clustering ; Bipolar-valued Similarity ; Meta-heuristic Algorithm
Abstract :	[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.
Permalink :	http://hdl.handle.net/10993/285
DOI :	10.1007/978-3-642-25853-4_2
Commentary :	LNAI 7120 Part I Advanced Data Mining and Applications ADMA 201 J. Tang et al. (Eds)

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access ADMA2011.pdf Author preprint 266.41 kB View/Open

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map