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In this work, we present a new two-stage technique to find clusters of different shapes, densities and sizes in the presence of overlapped clusters and noise. Firstly, a density-based clustering approach is developed using a density function estimated by the EM algorithm and in the second stage, a hierarchical strategy is used to merge clusters according to a dissimilarity measure here introduced in order to assess the overlap and proximity of the clusters. Several synthetic and real world data sets are used to evaluate the effectiveness and the efficiency of the new algorithm, indicating that it obtains satisfactory clustering results.

A Density-based Hierarchical Clustering Algorithm for Highly Overlapped Distributions with Noisy Points