An Algorithmic Approach of Particle Swarm Optimization (PSO) in Consensus Clustering

Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. Often similarity is assessed according to a distance measure. Clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Consensus Clustering is a method that provides quantitative evidence for determining the number and membership of possible clusters within a dataset. Consensus clustering can provide benefits beyond what a single clustering algorithm can achieve. Consensus clustering algorithms often: generate better clusterings; find a combined clustering unattainable by any single clustering algorithm. The particle swarm optimization (PSO) algorithm is an optimization method which tries to find the optimal solution through the simulation of some ideas drawn from fish schooling, bird flocking, and other social groups. In this paper, the Particle Swarm Optimization algorithm (PSO) is proposed to solve the consensus clustering problem. We find that the particle swarm clustering algorithm is efficient for this problem.