一种改进初始聚类中心选择的K-means算法

针对K-means算法中聚类结果易受初始聚类中心影响的缺点,提出一种改进初始聚类中心选择的算法.该算法不断寻找最大聚类,并利用距离最大的两个数据对象作为开始的聚类中心对该聚类进行分裂,如此反复,直到得到指定聚类中心个数.用KDD CUP99数据集对改进算法进行仿真实验,实验数据表明,用该算法获得的聚类中心进行聚类相对原始的K-means算法,能获得更好的聚类结果.

Improved Initial Clustering Center Selection Method for K-means Algorithm

In allusion to the disadvantage of the clustering result easily influenced by the initial clustering centers in the K-means algorithm,an improved algorithm about initial clustering centers selection is presented.The algorithm finds the largest cluster firstly,and then makes the cluster to split by used two data objects which have the maximum distance as the first clustering centers,repeat the above steps until the specified number of clustering centers are obtained.Compared to the original algorithm,the experiment result on KDD CUP99 dataset shows that the improved algorithm has a better clustering result.