An Approach Based on Tunicate Swarm Algorithm to Solve Partitional Clustering Problem

The tunicate swarm algorithm (TSA) is a newly proposed population-based optimizer for solving global optimization problems. TSA uses best solution in the population order improve intensification and diversification of tunicates. Thus, possibility finding better position search agents has increased. aim clustering algorithms to distributed data instances into some groups according similar dissimilar features instances. Therefore, with proper dataset will be separated minimum similarities. In this work, firstly, an approach based on partitional problem. Then, implemented ten different problems taken from UCI Machine Learning Repository, performance compared performances three well known such as fuzzy c-means, k-means k-medoids. experimental results comparisons show that highly competitive robust