Particle Subswarms Collaborative Clustering

Collaborative clustering aims to find a common data structure between several distributed data sets governed by different privacy constraints and technical limitations that prohibit a central collection of data for processing. Therefore, it is required to process the data sets separately using collaboration, which allows clustering algorithms to work locally on an individual data set while exchanging information about the finding with algorithms in other data locations. Thus, the different data locations share information to improve individual clustering result amidst technical and privacy limitations but without breaching privacy. In this article, we present a framework of collaborative clustering that does not require interaction coefficients to regulate the effect of collaboration. We further adapt the framework to cluster distributed data using crisp and fuzzy clustering algorithms. We use particle swarm optimization techniques to inference the framework and, therefore, call it particle subswarms. Moreover, the collaboration increases the number of particles in the swarm without increasing the number of clusters in the data set. This article, therefore, provides the theoretical foundations of particle subswarms and some experimental results on several data sets.