关键词: K-means算法, 聚类分割, 聚类删除, 局部迭代聚类, 聚类邻居

Abstract:

The K-means algorithm is one of the most popular and widely spread clustering methods. But it is not always possibly to find the appropriate initial value of the cluster centers, especially when the number of clusters is increased. If it can’t find suitable initial values, that will affect the clustering effect. This paper proposes an iterative approach to improve the quality of the clustering. This method called Partial Iterative Fast K-means plus-minus（PIFKM+?）. Based on the K-means clustering, the algorithm divides a cluster and removes another one, then re-clusters the affected data, in each iteration. The algorithm reduces the time complexity and improves the effect of clustering. The proposed method has the advantages of being able to update clusters quickly, is insensitive to initial values of cluster centers, and can improve clustering accuracy in the face of a large number of clusters. By comparing with the K-means and K-means++, experimental results vividly demonstrate that the algorithm has better clustering effect, higher operating efficiency and scalability on the simulation data sets and the real data sets. Through the statistical analysis of the final experimental results, it is shown that the PIFKM+? algorithm does not lose too much time efficiency while improving clustering accuracy.

Key words: K-means algorithm, cluster segmentation, cluster removing, partial iterative clustering, cluster neighbor