K. Chitra et al, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.8, August- 2017, pg. 109-115 © 2017, IJCSMC All Rights Reserved 109 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 6.017 IJCSMC, Vol. 6, Issue. 8, August 2017, pg.109 – 115 A Comparative Study of Various Clustering Algorithms in Data Mining K. Chitra 1 , Dr. D.Maheswari 2 Research Scholar, School of Computer Studies, Rathnavel Subramaniam College of Arts and Science, Sulur, Coimbatore 1 Head, Research Coordinator, School of Computer Studies- PG, Rathnavel Subramaniam College of Arts and Science, Sulur, Coimbatore 2 E mail: chitra.k@rvsgroup.com 1 , maheswari@rvsgroup.com 2 Abstract --The purpose of the data mining technique is to mine information from a bulky data set and make it into a reasonable form for supplementary purpose. Data mining can do by passing through various phases. Mining can be done by using supervised and unsupervised learning. Clustering is a significant task in data analysis and data mining applications. It is the task of arranging a set of objects so that objects in the identical group are more related to each other than to those in other groups (clusters). The clustering is unsupervised learning. Clustering algorithms can be classified into partition-based algorithms, hierarchical- based algorithms, density-based algorithms and grid-based algorithms. This paper focuses on a keen study of different clustering algorithms in data mining. A brief overview of various clustering algorithms is discussed. Keywords: data mining, clustering, clustering algorithms, techniques I. INTRODUCTION Data mining refers to extracting information from large amounts of data, and transforming that information into an understandable and meaningful structure for further use. Data mining is an essential step in the process of knowledge discovery from data (or KDD). It helps to extract patterns and make hypothesis from the raw data. Tasks in data mining include anomaly detection, association rule learning, classification, regression, summarization and clustering [1]. In Data Mining the two types of learning sets are used, they are supervised learning and unsupervised learning. a) Supervised Learning In supervised training, data includes together the input and the preferred results. It is the rapid and perfect technique. The accurate results are recognized and are given in inputs to the model through the learning procedure. Supervised models are neural network, Multilayer Perceptron and Decision trees.