G.Madhumitha et al, International Journal of Computer Science and Mobile Computing, Vol.7 Issue.8, August- 2018, pg. 192-195 © 2018, IJCSMC All Rights Reserved 192 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. 7, Issue. 8, August 2018, pg.192 – 195 A Survey on Clustering Techniques in Data Mining G.Madhumitha 1 , K.Kathiresan 2 ¹Student, Master of Engineering, Department of CSE, Angel College of Engineering & Technology, India ²Assistant Professor, Department of CSE, Angel College of Engineering & Technology, India 1 gmadhumitha7@gmail.com; 2 kathirpk@gmail.com Abstract— Data mining refers to the process of extracting information from a large amount of data and transforming it into an understandable form. Clustering is one of the most important methodology in the field of data mining. It is an unsupervised machine learning technique. Clustering means grouping a set of objects so that similar objects present in the same group and dissimilar objects present in different groups. This paper provides a broad survey on various clustering techniques and also analyzes the advantages and shortcomings of each technique. Keywords— Data mining, clustering, clustering analysis, clustering techniques, advantages and limitations I. INTRODUCTION This Data mining analyzes data from different perspectives and transforming it into an useful information [4]. The goal of data mining is the fast retrieval of data or information, discovering knowledge and identifying hidden patterns. Data mining involves various tasks such as anomaly detection, association rule learning, classification, regression and clustering analysis. In this paper, clustering analysis is done [10]. It is the process of dividing a set of data objects into subsets. Each subset is a cluster. The set of clusters resulting from a cluster analysis referred as clustering [8]. Clustering is used to group similar objects from a dataset. It leads to the discovery of previously unknown groups within the dataset. Clustering is also called data segmentation because clustering partitions large data sets into groups based on their similarity. Different clustering methods generate different clustering on the same data set. It is a fundamental operation in data mining. Fig 1 - Stages of Clustering Raw data Clustering algorithm Set of clusters