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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 data set. In experiments, we show that our approach also correctly detects clusters closely located to each other and clusters with wide density variations.

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