PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments

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. Key words: Ubiquitous Knowledge Discovery, Privacy Restrictions, Collaborative Clustering, Particle Swarm Optimization

• Type: Journal
• Year: 2011
• Where: IDA
• Authors: Benoît Depaire, Rafael Falcón, Koen Vanhoof, Geert Wets
• Keywords: Data Sites, IDA 2011, Information Technology, Particle Swarm Optimization, Ubiquitous Knowledge Discovery