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Clustering is the procedure to group similar objects together. Several algorithms have been proposed for clustering. Among them, the K-means clustering method has less time complexity. But it is sensitive to extreme values and would cause less accurate clustering of the dataset. However, K-medoids method does not have such limitations. But this method uses user-defined value for K. Therefore, if the number of clusters is not chosen correctly, it will not provide the natural number of clusters and hence the accuracy will be minimized. In this paper, the authors propose a grid based clustering method that has higher accuracy than the existing K-medoids algorithm.
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