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Multi-clustering: avoiding the natural shape of underlying metrics.
 

Summary: Multi-clustering: avoiding the natural shape
of underlying metrics.
Daniel Ashlock Eun-Youn-Kim
Mathematics and Statistics Department of Mathematics
University of Guelph Iowa State University
Guelph, Ontario Ames, Iowa, 50010
Canada N1G 2W1
Ling Guo
Bioinformatics and Computational Biology Program
Iowa State University
Ames, Iowa, 50010
Abstract
When a clustering algorithm is used, the results are strongly in-
fluenced by the choice of underlying distance measure. The Eu-
clidean metric rates a convex polytope as the most compact type
of object and builds clusters that are contained in compact poly-
topes. Presented here is a general method, multi-clustering, that
compensates for the intrinsic shape of a metric or similarity mea-
sure. The method is tested on data sets constructed to defeat
K-means clustering with the Euclidean metric. Multi-clustering

  

Source: Ashlock, Dan - Department of Mathematics and Statistics, University of Guelph

 

Collections: Mathematics