K-means算法初始聚类中心选择的优化

Optimization of Initial Clustering Centers Selection Method for K-Means Algorithm

迄今为止,在数据挖掘领域,人们已经实现了多种聚类算法,其中使用最广泛的当属K-means聚类算法.然而,在数据挖掘中,K-means算法面临的一个主要问题就是初始中心点选择问题.本文提出了一种结合关系矩阵和度中心性(Degree Centrality)的分析方法,从而确定K-means算法初始的k个中心点.与传统方法相比,本文算法可得到更加优质的聚类结果.实验结果表明该算法的有效性和可行性.

So far, in the field of data mining, people have achieved a variety of algorithms of clustering. And the most widely used is K-means clustering algorithm. But the main problem of K-means is the initial center selection problem. In this paper, a method is proposed to determine the initial K centers of the K-means algorithm through the relationship matrix and the Degree Centrality. Compared with the traditional algorithm, the proposed algorithm can get the better clustering result. Experimental results have proved the validity and feasibility of this algorithm.