Assessment of Clustering Techniques in Data Mining

Cluster study or clustering is the assignment of assigning a set of data into groups called clusters. Major task of clustering are explorative data mining, and a frequent methodology for statistical data analysis used in different fields, pattern recognition, image analysis, including machine learning, information retrieval, and bioinformatics. Cluster can be accomplished by diverse algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters embrace groups with low distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. There are numerous no of classification mechanisms that can be used such as K-nearest neighbor, Bayesian network, Neural Networks, Decision Trees, Support Vector Machine, and Boosting etc. This paper also compacts with a study on some of these commonly used techniques that are being widely used