关键词: k均值算法, 初始聚类中心, 高密度集, 最大距离积法, 聚类指数

Abstract:

The traditional K-means clustering algorithm initializing cluster centers randomly leads to the difference of clustering results.In the actual cases,it is difficult to give the exact number of clusters k to the algorithm in advance.In view of this,an improved K-means algorithm is presented based on density and clustering index.The algorithm generates the high density set named High Point(HP) according to all the objects' densities and chooses the mean of the furthest mutual distance two sample objects couples from HP as the first cluster's initial centers.The new initial center from rest objects in HP can be obtained by the maximum distances product algorithm.At the same time,the new algorithm automatically analyzes the clustering quality as the growth of the k values and determines the optimal number of clusters by selecting the index.Experiments show that the new algorithm achieves higher accuracy and validity.

Key words: K means algorithm;initial clustering center;high density set;maximum distances product;clustering index