Improved K-means Clustering Algorithm by Combining with Multiple Factors

Abstract

The traditional K-means clustering algorithm has the problems that the number of clusters needs to be determined artificially, the clustering results are easily affected by the initial clustering centers and isolated points, and the iterative process is computationally complicated. To address above problems, an improved K-means clustering algorithm combining multi-point optimization(MFK-means) is proposed. The proposed algorithm is improved from the following four aspects. Firstly, the number of clusters is jointly determined by combining the contour coefficient method and the elbow rule. Secondly, the optimized outlier detection algorithm is used to exclude the influence of isolated points on the clustering results. Thirdly, the initial clustering centers are gradually determined based on the outlier candidate set and the maximum-minimum distance idea. Finally, the heuristic method is used to reduce the computational effort of the iterative process. The experimental results on the UCI dataset show that the proposed improved algorithm has higher clustering accuracy and better stability than the traditional K-means algorithm and another improved algorithm.