Automatic Clustering Using Improved Imperialist Competitive Algorithm

Imperialist Competitive Algorithm (ICA) is considered as prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA, at run time, the suggested method (ACICA) finds the optimum number of clusters while optimal clustering of the data simultaneously .To increase the accuracy and speed of convergence, the structure of ICA changes. The proposed algorithm requires no background knowledge to classify the data. In addition, the proposed method is more accurate in comparison with other clustering methods based on evolutionary algorithms. DB and CS cluster validity measurements are used as the objective function. To demonstrate the superiority of the proposed method, the average of fitness function and the number of clusters determined by the proposed method is compared with three automatic clustering algorithms based on evolutionary algorithms.