关键词: 聚类, K均值算法, 聚类中心, 密度函数

Abstract: K-means is a widely used clustering method in many communities. However, the initial procedure affects the clustering results seriously, especially the initial centroids.  Reasonable initial centroids should be in the region with high data density, so an improved k-means initialization method is proposed based on local data density. Firstly, a definition of local data density function is given, and then initial centroids are chosen based on this definition. Experimental result shows that the proposed method has several advantages： it can find the data densities effective and the reasonable candidates of initial centroids, it shows outstanding performance when the number of categories is related large, it is robust to outliers and noisy, it is easy to implement.

Key words: clustering, k-means, initialization, data density