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- W2097540951 abstract "Discovering clusters of varyingly shapes, sizes and densities in a data set is still a challenging problem for density-based 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." @default.
- W2097540951 created "2016-06-24" @default.
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- W2097540951 date "2011-03-01" @default.
- W2097540951 modified "2023-09-27" @default.
- W2097540951 title "Density-based clustering using fuzzy proximity relations" @default.
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- W2097540951 doi "https://doi.org/10.1109/nafips.2011.5751999" @default.
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