A New Approach of Data Clustering by Improved ACA with Fuzzy Similarity

An approach of data clustering based on improved ACA with fuzzy similarity (ACA-Cluster) is presented. Combined with the global distribution and gradual evolution of improved ACA, we assign distribution rate as heuristic function to accelerate convergence. Performance of the algorithm is compared with K-means and LF to demonstrate efficiency and quality.