Initializing the Fuzzy C-Means Cluster Center with Particle Swarm Optimization for Sentiment Clustering

Abstract

Fuzzy C-Means (FCM) is one of the best-known clustering algorithms, however, FCM is significantly sensitive to the initial cluster center values and easily trapped in a local optimum. To overcome this problem, this study proposes and improved FCM with Particle Swarm Optimization (PSO) algorithm for high dimensional and unstructured sentiment clustering. PSO is applied for the determination of better cluster center initials. The results showed that FCM-PSO can provide better performance compared to the conventional FCM in terms of Rand Index, F-measure and Objective Function Values (OFV). The better OFV value indicates that FCM-PSO requires faster convergence time and better noise handling.