A Novel Clustering Algorithm Using Kmeans 'CUK'

While K-means is one of the most well known methods to partition data set into clusters, it still has a problem when clusters are of different size and different density. K-means converges to one of many local minima. Many methods have been proposed to overcome these limitations of K-means, but most of these methods do not overcome the limitation of both different density and size in the same time. The previous methods success to overcome one of them while fails with the others. In this paper we propose a novel algorithm of clustering using K-means (CUK). Our proposed algorithm uses K-means to cluster data objects by using one additional centroid, several partionining and merging process are used. Merging decision depends on the average mean distance where average distance between each cluster mean and each data object is determined, since the least and closet clusters in average mean distance are merged in one cluster, this process continues until we get the final required clusters in an accurate and efficient way. By comparing the results with K-means, it was found that the results obtained by the proposed algorithm CUK are more effective and accurate.