A new method for clustering based on development of Imperialist Competitive Algorithm

Clustering is one of the most widely used data mining techniques that can be used to create homogeneous clusters. K-means is one of the popular clustering algorithms that, despite its inherent simplicity, has also some major problems. One way to resolve these problems and improve the k-means algorithm is the use of evolutionary algorithms in clustering. In this study, the Imperialist Competitive Algorithm (ICA) is developed and then used in the clustering process. Clustering of IRIS, Wine and CMC datasets using developed ICA and comparing them with the results of clustering by the original ICA, GA and PSO algorithms, demonstrate the improvement of Imperialist competitive algorithm.