An Optimized k-means Algorithm for Selecting Initial Clustering Centers

Selecting the initial clustering centers randomly will cause an instability final result, and make it easy to fall into local minimum. To improve the shortcoming of the existing k-means clustering center selection algorithm, an optimized k-means algorithm for selecting initial clustering centers is proposed in this paper. When the number of the sample's maximum density parameter value is not unique, the distance between the plurality samples with maximum density parameter values is calculated and compared with the average distance of the whole sample sets. The k optimized initial clustering centers are selected by combing the algorithm proposed in this paper with maximum distance means. The algorithm proposed in this paper is tested through the UCI dataset. The experimental results show the superiority of the proposed algorithm.