Content driven clustering algorithm combining density and distance functions

Abstract Density and distance based clustering are two distinct approaches to the same problem. In this contribution, a novel algorithm is presented in order to exploit the benefits of both approaches. This is achieved, not by combining those approaches into a single notion, but by utilizing the advantages of each one, depending on what each step of the algorithm aims to achieve. To be precise, the Window Density Function is utilized to provide regions of high density and hence a region of clusters or a part of a cluster. Affinity Propagation is, consequently, utilized to provide a group of clusters within such a region. Finally, these regions are merged to form actual clusters. The proposed methodology is tested on a variety of synthetic and real-life datasets. The algorithm presented in this contribution outperforms other well-known algorithms, with which it is compared to, in the majority of the datasets used.

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