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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11628

Title: PSO driven collaborative clustering: a clustering algorithm for ubiquitous environments
Authors: DEPAIRE, Benoit
WETS, Geert
Issue Date: 2011
Publisher: IOS Press
Citation: Intelligent Data Analysis, 15(1). p. 49-68
Abstract: The goal of this article is to introduce a collaborative clustering approach to the domain of ubiquitous knowledge discovery. This clustering approach is suitable in peer-to-peer networks where different data sites want to cluster their local data as if they consolidated their data sets, but which is prevented by privacy restrictions. Two variants exist, i.e. one for data sites with the same observations but different features and one for data sites with the same features but different observations. The technique contains two parts, i.e. a collaborative fuzzy clustering technique and a particle swarm optimization to optimize the collaboration between data sites. Empirical analysis show how and when this PSO-CFC approach outperforms local fuzzy clustering.
URI: http://hdl.handle.net/1942/11628
DOI: 10.3233/IDA-2010-0455
ISI #: 000286604500004
ISSN: 1088-467X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

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