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- W2000225629 abstract "In this paper a new possibilistic clustering algorithm is proposed, where certain critical parameters are dynamically adjusted, allowing for increased flexibility in uncovering the clustering structure of the data. The new algorithm requires only a crude overestimation of the number of clusters (instead of the actual number of them, as many other well-known algorithms require), and has - in principle - the ability to reduce this number to that of the clusters, that are naturally formed by the data. In addition, since the proposed clustering algorithm is a possibilistic one, it is expected that it will provide dense in data points regions as clusters. Experimental results, on both synthetic and real data sets, verify the previous conclusions." @default.
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- W2000225629 date "2013-12-01" @default.
- W2000225629 modified "2023-09-25" @default.
- W2000225629 title "Adaptive possibilistic clustering" @default.
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- W2000225629 doi "https://doi.org/10.1109/isspit.2013.6781918" @default.
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