Comparative Study on Hierarchical and Partitioning Data Mining Methods

Data mining is the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful and ultimately understandable patterns in data. The goal of this paper is to study hierarchical along with partitioning method and its recent issues and present a comparative study on the above mentioned clustering techniques that are related to data mining. This paper presents a tutorial overview of the main clustering methods used in data mining. The goal is to provide a self contained review of the concepts and the mathematics underlying clustering techniques along with some experimental results. Paper begins by providing some measures and criteria that are used for determining whether two objects are similar or dissimilar. Further on the paper explores the study of clustering methods with some experimental results which is a study based experimental conclusion. Finally we conclude our paper with the problems we faced during dealing with the cluster classification and in getting the output.