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.