Combination of Fuzzy C-Means and Particle Swarm Optimization for Text Document Clustering

Document clustering, an important tool for document organization and browsing, has become an active field of research in the machine learning community. Fuzzy c-means (FCM), a powerfully unsupervised clustering algorithm, has been widely used for categorization problems. However, as an optimization algorithm, it easily leads to local optimal clusters. Particle swarm optimization (PSO) algorithm is a stochastic global optimization technique. This paper presents a hybrid approach for text document clustering based on fuzzy c-means and particle swarm optimization (PSO-FCM), which makes full use of the merits of both algorithms. The PSO-FCM not only helps the FCM clustering escape from local optima but also overcomes the shortcoming of the slow convergence speed of the PSO algorithm. Experimental results on two commonly used data sets show that the proposed method outperforms than that of FCM and PSO algorithms.