Particle Swarm Optimization for Clustering Ensemble

Abstract

Clustering ensemble is an open proposition that aims to solve the limitation of a single cluster algorithm on the diversity of data structures in data partitions. It obtains a consensus model by integrating multiple clustering algorithms. Compared with a single clustering algorithm, it can process the data more accurately and stably, and has a better tolerance for the structure and diversity of the data. This paper proposes to integrate particle swarm optimization for clustering ensemble model. Particularly, the idea is to first use multiple classic clustering algorithms to cluster the original data set to obtain a preliminary clustering result set, and then search for a clustering result that is most consistent with the preliminary results by a particle swarm optimization procedure. In the experiments, we make use of public data sets and micro-precision to evaluate the effectiveness of the proposed clustering ensemble model, and compare it with both base clustering algorithms and other common clustering ensemble algorithms. The evaluation results show that this model can significantly improve the base clustering algorithms and outperforms common clustering ensemble algorithms in most cases.