Data mining is the breakthrough of identifying the hidden patterns. Data mining is sorting through data to finding the patterns, anomalies and correlations within large data sets to predict outcomes. Clustering is the main task of exploratory data analysis and data mining applications. Clustering is a data mining technique used to place data elements into related groups without advance knowledge of the group definitions and it is the process of making a group of abstract objects into classes of similar objects.clusters has the creditability to pinpointing the natural groupings of cases based on a set of attributes clustering is used in various application in the real world, Such as data/text mining, voice mining,Image processing, web mining.Clustering can be done by various of algorithms such as hierarchical, partitioning, grid and density based algorithms. In this paper, a review of diversified clustering techniques in data mining is presented.
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