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title:
 
CEVCLUS: Constrained evidential clustering of
publication:
 
EUSFLAT
part of series:
  Advances in Intelligent Systems Research
pages:   876 - 882
DOI:
   To be assigned soon (how to use a DOI)
author(s):
 
proximity data
publication date:
 
July 2011
keywords:
 
Semi-supervised clustering, pairwise constraints, belief functions, evidence theory, proximity data.
abstract:
 
We present an improved relational clustering method integrating prior information. This new algorithm, entitled CEVCLUS, is based on two concepts: evidential clustering and constraint-based clustering. Evidential clustering uses the DempsterShafer theory to assign a mass function to each object. It provides a credal partition, which subsumes the notions of crisp, fuzzy and possibilistic partitions. Constraint-based clustering consists in taking advantage of prior information. Such background knowledge is integrated as an additional term in the cost function. Experiments conducted on synthetic and real data demonstrate the interest of the method, even for unbalanced datasets or non-spherical classes.
copyright:
  
© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text:
 
eusflatlfa2011_RS_9.pdf (1307 K)