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Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the KMeans algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed KMeans algorithm is compared with KMeans, Static Weighted KMeans (SWKMeans) and Dynamic Weighted KMeans (DWKMeans) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed KMeans algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
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