AN EFFICIENT COST FUNCTION FOR IMPERIALIST COMPETITIVE ALGORITHM TO FIND BEST CLUSTERS
Mojgan Ghanavati, Mohammad Reza Gholamian, Behrouz Minaei Bidgoli, Mehran Davoudi · 2011
Cluster analysis is one of the attractive data mining techniques that have been used in many fields. One of the popular types of clustering algorithms is the center based clustering algorithm. K-means used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, Kmeans has two shortcomings. K-means is dependent on the initial state and convergence to local optima in some of the large problems. In order to these shortcomings, in an unsupervised clustering the number of clusters needs to be fixed by a human analyst too. In order to overcome local optima problem and for determining the number of clusters, lots of studies done in clustering. In this paper we use a new search heuristic called “Imperialist Competitive Algorithm 1 ” to find the best clusters with best numbers of clustering. In this algorithm we assume each clustering solution with special clusters number as a country and use a new cost function to calculate the clustering cost in each step. We compared proposed algorithm with other heuristics algorithm in clustering, such as traditional K-means, IGKA, CSO and GA-PSO by implementing them on several well-known datasets. Our findings show that the proposed algorithm works better than the others according to cost function and standard deviation.