2011
DOI: 10.1109/nafips.2011.5751999
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Abstract: Discovering clusters of varyingly shapes, sizes and densities in a data set is still a challenging problem for densitybased algorithms. Recently presented approaches either require the input parameters involving the information about the structure of the data set, or are restricted to two-dimensional data. In this paper, we present a density-based clustering algorithm, which uses the fuzzy proximity relations between data objects for discovering differently dense clusters without any a-priori knowledge of a d…

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