Clustering is the process of organizing objects into several groups whose members are similar in some way and is very important technique in data mining as it has applications spread extensively example marketing, biology, pattern recognition etc. Various algorithms have been proposed, published, implemented for clustering like the one published by Rodriguez and Liao but this algorithm is dependent and sensitive to specified parameters and also faces difficulties in identification of ideal problems. Another one published by G. Wang and Q. Song this algorithm do not list all possible numbers of the nearest neighbors and the accuracy is not better in terms of Olivetti face data set then impact of this on performance. This paper overcome the problem faces by above algorithm so, here proposes a new clustering method that will identify cluster centers automatically via statistical testing. Here first define a new metric to evaluate the local density of an object which is named K-density and second metric is define to evaluate the distance of an object to its neighbors with higher density. Then, product of these two metrics is used to evaluate the centrality of each object. After analyzing the distribution of these metrics further transformed the clustering center identification into a problem of extreme-value detection from a long-tailed distribution Finally, apply outward statistical testing method to detect the clustering centers automatically and then completed the clustering process by assigning each of the rest objects to the cluster that contains its nearest neighbor with higher K-density.

Clustering, Clustering Center Identification, Long-tailed Distribution, Outward Statistical Testing

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