C. Ding, X. He, H. Zha, and H. Simon (USA)
We demonstrate that data clustering amounts to a dynamic process of self-aggregation in which data objects move to wards 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.
Important Links:
Go Back
