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 FALCÓN MARTINEZ, Rafael VANHOOF, Koen WETS, Geert |
Issue Date: | 2011 | Publisher: | IOS Press | Source: | 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. | Keywords: | Ubiquitous knowledge discovery, privacy restrictions, collaborative clustering, particle swarm optimization | Document URI: | http://hdl.handle.net/1942/11628 | ISSN: | 1088-467X | e-ISSN: | 1571-4128 | DOI: | 10.3233/IDA-2010-0455 | ISI #: | 000286604500004 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2012 |
Appears in Collections: | Research publications |
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DEPAIRE2011A.PDF | Non Peer-reviewed author version | 368.04 kB | Adobe PDF | View/Open |
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