Comparative Analysis of Various Clustering Algorithms Using WEKA
Priyanka Sharma · 2015
Clustering is an unsupervised learning problem which is used to determine the intrinsic grouping in a set of unlabeled data. Grouping of objects is done on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity in such a way that the objects in the same group/cluster share some similar properties/traits. There is a wide range of algorithms available for clustering. This paper presents a comparative analysis of various clustering algorithms. In experiments, the effectiveness of algorithms is evaluated by comparing the results on 6 datasets from the UCI and KEEL repository.