An Improved Initial Clustering Center Selection Method for K-Means Algorithm

Abstract

Clustering result is easily influenced by the initial clustering centers in the K-means algorithm,an improved algorithm about initial clustering centers selection is presented.The algorithm finds the maximun Euclidean distance of cluster firstly,and then makes the cluster to split by used two data objects which have the maximum distance as new clustering centers,repeat the above steps until the specified number of clustering centers are obtained.Compared to the original algorithm,the improved algorithm can solve the problem of the instability of clustering effect generated by randomness, and its time complexity was also decreased.