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Comparative Analysis of Clustering Techniques for Real Dataset

Clustering is a process of classification of data objects into similar groups or clusters. A clustering algorithm partitions the data set into several similar groups. By analyzing different algorithms, efficiency of clustering techniques has been calculated. System can define cluster quality by comparing different clustering algorithms. Data clustering techniques are used in a wide variety of scientific applications such as biology, pattern recognition, information systems etc. In this paper an attempt has been made to compare the different data clustering techniques such as K-Means, KMedoid and Rough K-Means on the basis of various parameters like memory required, execution time and compactness of the cluster. These algorithms are applied on the real dataset.