Self-aggregation in scaled principal component space
- LBNL Library
Automatic grouping of voluminous data into meaningful structures is a challenging task frequently encountered in broad areas of science, engineering and information processing. These data clustering tasks are frequently performed in Euclidean space or a subspace chosen from principal component analysis (PCA). Here we describe a space obtained by a nonlinear scaling of PCA in which data objects self-aggregate automatically into clusters. Projection into this space gives sharp distinctions among clusters. Gene expression profiles of cancer tissue subtypes, Web hyperlink structure and Internet newsgroups are analyzed to illustrate interesting properties of the space.
- Research Organization:
- Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (US)
- Sponsoring Organization:
- USDOE Director, Office of Science. Office of Advanced Scientific Computing Research. Mathematical, Information, and Computational Sciences Division (US)
- DOE Contract Number:
- AC03-76SF00098
- OSTI ID:
- 820779
- Report Number(s):
- LBNL--49048
- Country of Publication:
- United States
- Language:
- English
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