  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 K-Means algorithm is
implemented by three distance functions and to identify the optimal distance
function for clustering methods. The proposed K-Means algorithm is compared
with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means
(DWK-Means) algorithm by using Davis Bouldin index, Execution Time and
Iteration count methods. Experimental results show that the proposed K-Means
algorithm performed better on Iris and Wine dataset when compared with other
three clustering methods.
