The search functionality is under construction.
The search functionality is under construction.

Divergence-Based Geometric Clustering and Its Underlying Discrete Proximity Structures

Hiroshi IMAI, Mary INABA

  • Full Text Views

    0

  • Cite this

Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E83-D No.1 pp.27-35
Publication Date
2000/01/25
Publicized
Online ISSN
DOI
Type of Manuscript
Special Section INVITED PAPER (Special Issue on Surveys on Discovery Science)
Category

Authors

Keyword