For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures
Hiroshi IMAI Mary INABA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2000/01/25
Print ISSN: 0916-8532
Type of Manuscript: INVITED PAPER (Special Issue on Surveys on Discovery Science)
unsupervised learning, geometric clustering, Voronoi diagram, computational geometry, information geometry,
Full Text: PDF(349.8KB)>>
This paper surveys recent progress in the investigation of the underlying discrete proximity structures of geometric clustering with respect to the divergence in information geometry. Geometric clustering with respect to the divergence provides powerful unsupervised learning algorithms, and can be applied to classifying and obtaining generalizations of complex objects represented in the feature space. The proximity relation, defined by the Voronoi diagram by the divergence, plays an important role in the design and analysis of such algorithms.