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Erschienen in:
2002 | OriginalPaper | Buchkapitel
Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*
verfasst von : Chris Ding, Xiaofeng He, Hongyuan Zha, Horst Simon
Erschienen in: Principles of Data Mining and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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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. Selfaggregation 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.