ASCM: An accelerated soft c-means clustering algorithm

The advantages of soft c-means over its hard and fuzzy versions render it more attractive to use in a wide variety of applications. Its main merit lies in its relatively higher convergence speed, which is more obvious in the presence of huge high dimensional data. This work presents a new approach to accelerate the convergence of the original soft c-means. It is mainly based on an iterative optimization approach and a relaxation technique. Several low and high dimensional datasets are used to evaluate the performance of the proposed approach. Experimental results show up to 70% improvement over the original soft and fuzzy c-means algorithms.