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- W1553652103 abstract "Considering data processing problems from a geometric point of view, previous work has shown that the intrinsic dimension of the data could have some semantics. In this paper, we start from the consideration of this inherent topology property and propose the usage of such a semantic criterion for clustering. The corresponding learning algorithms are provided. Theoretical justification and analysis of the algorithms are shown. Promising results are reported by the experiments that generally fail with conventional clustering algorithms." @default.
- W1553652103 created "2016-06-24" @default.
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- W1553652103 date "2006-01-01" @default.
- W1553652103 modified "2023-09-27" @default.
- W1553652103 title "Clustering with a Semantic Criterion Based on Dimensionality Analysis" @default.
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- W1553652103 doi "https://doi.org/10.1007/11893257_88" @default.
- W1553652103 hasPublicationYear "2006" @default.
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