A K-means Algorithm Based on Optimizing the Initial Clustering Center and Determining the K Value
JIANG Li
XUE Shanliang
Abstract：Two parameters in the K-means algorithm need to be input,the one is the number of the K which is needed to clus?tering and the other is the initial clustering center. Selecting the initial cluster centers has a large impact on the clustering results in the algorithm of the K-means,the traditional K-means clustering algorithm selects the clustering center randomly,while randomly select the cluster center will inevitably take the outlier point,this has a large impact on the clustering results. The number of K is in?puted by users,a bad K also has a large impact on the on the clustering results. This paper proposes an improved K-means cluster?ing algorithm that based on the density of the thought ,firstly divides the clustering samples into core point,border point and outlier point,then delete the border point and outlier point from the clustering samples and select the clustering center by using the center of clustering samples,the test shows that the improved algorithm has more stability than before.
Keywords：K-means clusteringclustering numberclustering centerdensityoutlier point
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 21-24,113 )