Document Detail

A nonparametric algorithm for detecting clusters using hierarchical structure.
MedLine Citation:
PMID:  21868905     Owner:  NLM     Status:  In-Data-Review    
The present paper discusses a nonparametric algorithm for detecting clusters. In the algorithm a positive value called potential is associated with each datum based on dissimilarities. By defining subordination relations among data, hierarchical structure is introduced into the data set. As a result of the introduction of hierarchical structure, the data set is divided into some subsets called subclusters. A procedure for constructing clusters from the subclusters is also considered. The proposed algorithm can be applied to a very wide range of data set and has great ability to detect clusters, which is verified by computer simulation.
R Mizoguchi; M Shimura
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  2     ISSN:  0162-8828     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  1980 Apr 
Date Detail:
Created Date:  2011-08-26     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9885960     Medline TA:  IEEE Trans Pattern Anal Mach Intell     Country:  United States    
Other Details:
Languages:  eng     Pagination:  292-300     Citation Subset:  -    
MEMBER, IEEE, Institute of Scientific and Industrial Research, Osaka University, Suita, Osaka, Japan.
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