Comparison of K-Means Algorithm and Hierarchical Algorithm using Weka Tool

Abstract: Data mining is a process of Collecting useful information and patterns from huge data. Clustering is a process of partitioning a set of data or objects into a set of meaningful sub-classes, called clusters. In clustering, objects of the data set are grouped into clusters, such a way that each group are very different from each other and the objects in the same group are very similar to each other. In this paper analyses two major clustering algorithms: K-Means and Hierarchical. The performance of these two clustering algorithms is compared using the clustering toolkit Weka, which is a platform-independent open source toolkit.

Keywords: Data mining, Clustering, Clustering algorithms, K-means algorithms, Hierarchical clustering and Weka toolkit