A Kernel Based Clustering Algorithm using Particle Swarm Optimization

Unsupervised learning is one of the major research areas in machine learning, while kernel methods provide eficient solutions for various statistical learning problems. In this paper we propose a kernel based clustering algorithm that uses the Particle Swarm Optimization technique and discriminant functions. The method represents a general framework for solving the clustering problem: once an appropriate clustering validation index is chosen for a given class of datasets, the method performs very well in solving the problem. The method automatically detects the clusters in a given dataset and also, automatically estimates the number of clusters. Due to the use of kernel functions, our approach can be used for both linearly separable and linearly non-separable clusters. Since our algorithm uses the Particle Swarm Optimization technique, parallel computation may be used, if necessary. We evaluate our method on various datasets and we discuss its capabilities.