PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments

Authors:
Benoît Depaire;Rafael Falcón;Koen Vanhoof;Geert Wets
Affiliations:
(Correspd. E-mail: benoit.depaire@uhasselt.be) Data Analysis and Modeling, Hasselt University, Diepenbeek, Belgium;School of Information Technology and Engineering (SITE), University of Ottawa, Ottawa, ON, Canada;Data Analysis and Modeling, Hasselt University, Diepenbeek, Belgium;Data Analysis and Modeling, Hasselt University, Diepenbeek, Belgium
Venue:
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Year:
2011

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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.