期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2013
卷号：2
期号：8
页码：2398-2401
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Clustering analysis is a descriptive task that seeks to identify homogeneous groups of objects based on the values of their attributes. K-medoid clustering algorithms are widely used for many practical applications. Original K-medoid algorithm select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it generates unstable and empty clusters which are meaningless. The original k-means algorithm is computationally expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations. Improved k-Medoid clustering algorithm has the accuracy higher than the original
