关键词: 聚类, k-medoids算法, 密度初始化, 目标函数

Abstract: For the disadvantages that sensitivity to centers initialization, lower clustering accuracy and slow convergent speed of k-medoids algorithm, a novel k-medoids algorithm based on density initialization, density of iterative search strategy and optimization criterion function is proposed. The Initialization of the algorithm is that, it chooses k cluster centers in the high-density area which are far apart, effectively positioning of the final cluster center. To replace the centers are in the ranges which are proximity to the k-initial centers, to reduce the scope of the search candidate point. Criterion function of equalization based on class density and within-class density weighted is adopted to improve the clustering precision. Experimental results show that this algorithm can improve the clustering quality, shorten the clustering time compared with traditional k-medoids algorithms or other improved algorithms.

Key words: clustering, k-medoids algorithm, density initialization, criterion function