Adaptive K-means Algorithm with Dynamically Changing Cluster Centers and K-Value

摘要：

　　In allusion to the disadvantage of having to obtain the number of clusters in advance and the sensitivity to selecting initial clustering centers in the K-means algorithm,an improved K-means algorithm is proposed,that the cluster centers and the number of clusters are dynamically changing.The new algorithm determines the cluster centers by calculating the density of data points and shared nearest neighbor similarity,and controls the clustering categories by using the average shared nearest neighbor self-similarity.The experimental results of IRIS testing data set show that the algorithm can select the cluster cennters and can distinguish between different types of cluster efficiently.

关键词： K-means algorithm density nearest neighbor similarity self-similarity

作者: DENG Ai-ping XIAO Ben YUAN Hui-yong

作者单位: Hunan Institute of Humanities, Science and Technology, Loudi Hunan, China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1373-1377

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）