关键词: 聚类, K-means算法, 密度, 聚类中心, 高密度区域

Abstract: The traditional K-means algorithm has sensitivity to the initial centers.To solve this problem，an improved K-means algorithm based on density is presente.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.Theory analysis and experimental results demonstrate that the improved algorithm can get better clustering .and eliminate the sensitivity to the initial start centers.

Key words: clustering, K-means algorithm, density, clustering center, high density area