Comparative Clustering Algorithms on Integrated Dataset | Original Article

ABSTRACT:

Clustering is an unsupervised learning problem which is used to determine the intrinsic grouping in a set of unlabeled data and also applied in the preprocessing of datasets resulting further improvement in the next task such as classification. While clustering, 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 groupcluster share some similar propertiestraits. There is a wide range of algorithms available for clustering in various data mining tools. This paper presents a comparative analysis of four clustering algorithms in classes to cluster evaluation mode against the three datasets where one of them is integrated. In experiments, the effectiveness of algorithms is evaluated by comparing the results among the datasets and algorithms.