Fuzzy Clustering Using Automatic Particle Swarm Optimization

Fuzzy clustering is a popular unsupervised learning method used in cluster analysis which allows a data point to belong to two or more clusters. Fuzzy c-means is one of the most well-known and used methods, however, the number of clusters need to be defined in advance. This paper proposes a clustering approach based on Particle Swarm Optimization. This approach automatically determines the optimal number of clusters using a threshold vector that is added to the particle. The algorithm starts by partitioning the data set randomly within a preset maximum number of clusters in order to overcome the fuzzy c-means shortcoming of the predefined cluster count. A reconstruction criterion is applied to evaluate the performance of the clustering results of the proposed algorithm. The experiments conducted show that the proposed algorithm can automatically find the optimal number of clusters.