Unsupervised Learning: Self-aggregation in Scaled Principal Component Space

Authors:
Chris Ding;Xiaofeng He;Hongyuan Zha;Horst D. Simon
Affiliations:
-;-;-;-
Venue:
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Year:
2002

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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.