K-means Clustering Algorithm with Meliorated Initial Center
Song Xin · Jisuanji gongcheng · 2007
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.