Document Detail


Detecting the number of clusters in n-way probabilistic clustering.
MedLine Citation:
PMID:  20847390     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
Recently, there has been a growing interest in multiway probabilistic clustering. Some efficient algorithms have been developed for this problem. However, not much attention has been paid on how to detect the number of clusters for the general n-way clustering (n ≥ 2). To fill this gap, this problem is investigated based on n-way algebraic theory in this paper. A simple, yet efficient, detection method is proposed by eigenvalue decomposition (EVD), which is easy to implement. We justify this method. In addition, its effectiveness is demonstrated by the experiments on both simulated and real-world data sets.
Authors:
Zhaoshui He; Andrzej Cichocki; Shengli Xie; Kyuwan Choi
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on pattern analysis and machine intelligence     Volume:  32     ISSN:  1939-3539     ISO Abbreviation:  IEEE Trans Pattern Anal Mach Intell     Publication Date:  2010 Nov 
Date Detail:
Created Date:  2010-09-17     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:  2006-21     Citation Subset:  IM    
Affiliation:
RIKEN Brain Science Institute, Wako-shi, Saitama, Japan. he_shui@tom.com
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