Performance Evaluation of Clustering Algorithm Using Different Datasets

Abstract

With the advancement of technology, Cluster analysis plays an important role in analyzing text mining techniques. It divides the dataset into several meaningful clusters to reflect the dataset’s natural structure. In this paper we analyze the four major clustering algorithms namely Simple K-mean, DBSCAN, HCA and MDBCA and compare the performance of these four clustering algorithms. Performance of these four techniques are presented and compared using a clustering tool WEKA. The results are tested on different datasets namely Abalone, Bankdata, Router, SMS and Webtk dataset using WEKA interface and compute instances, attributes and the time taken to build the model. I have also highlighted the advantages, disadvantages and applications of each clustering technique.

Keywords: Density based clustering algorithm; Hierarchical clustering algorithm; Make density based clustering; Simple K-mean.