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Local Density Estimation based Clustering

In this paper we propose a density based clustering approach. A kernel based density estimation technique is used to estimate the density of the given data set using a Gaussian kernel. Generally, a fixed width parameter is used for all the Gaussians in such methods. Here, a method to automatically determine the widths of Gaussians by considering the information available locally at a data point has been proposed. Cluster boundary information is subsequently extracted from the estimated density of the data. The performance of the proposed method is demonstrated on several data sets. Studies comparing the performance of the proposed method with that of DBSCAN and SVC are also presented.