摘要： 传统的k-means算法对初始聚类中心敏感，聚类结果随不同的初始输入而波动。为消除这种敏感性，提出一种优化初始聚类中心的方法，此方法计算每个数据对象所在区域的密度，选择相互距离最远的k个处于高密度区域的点作为初始聚类中心。实验表明改进后的k-means算法能产生质量较高的聚类结果，并且消除了对初始输入的敏感性。

关键词: 数据挖掘, 聚类, k-means算法, 聚类中心

Abstract: The traditional k-means algorithm has sensitivity to the initial start center. To solve this problem, a new method is proposed to find the initial start center. First it computes the density of the area where the data object belongs to; then finds k data objects all of which are belong to high density area and the most far away to each other, using these k data objects as the initial start centers. Experiments on the standard database UCI show that the proposed method can produce a high purity clustering result and eliminate the sensitivity to the initial start centers.

Key words: Data mining, Clustering, K-means algorithm, Clustering center