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- W2111101684 abstract "We demonstrate that data clustering amounts to a dynamic process of self-aggregation in which data objects move towards each other to form clusters, revealing the inherent pattern of similarity. Self-aggregation is governed by connectivity and occurs in a space obtained by a nonlinear scaling of principal component analysis (PCA). The method combines dimensionality reduction with clustering into a single framework. It can apply to both square similarity matrices and rectangular association matrices." @default.
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- W2111101684 date "2002-01-01" @default.
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- W2111101684 title "Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*" @default.
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- W2111101684 doi "https://doi.org/10.1007/3-540-45681-3_10" @default.
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