期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2012
卷号：3
期号：3
页码：4119-4121
出版社：TechScience Publications
摘要：As in today’s world large number of modern techniques is evolving for scientific data collection, large number of data is getting accumulated at various databases. Systematic data analysis methods are in need to gain/extract useful information from rapidly growing databanks. Clustering analysis method is one of the main analytical methods in data mining; in which k-means clustering algorithm is most popularly/widely used for many applications. Clustering algorithm is divided into two categories: partition and hierarchical clustering algorithm. This paper discusses one partition clustering algorithm (kmeans) and one hierarchical clustering algorithm (agglomerative). K-means algorithm has higher efficiency and scalability and converges fast when dealing with large data sets. Hierarchical clustering constructs a hierarchy of clusters by either repeatedly merging two smaller clusters into a larger one or splitting a larger cluster into smaller ones. Using WEKA data mining tool we have calculated the performance of k-means and hierarchical clustering algorithm on the basis of accuracy and running time.
