Clustering interval-valued proximity data using belief functions

  title={Clustering interval-valued proximity data using belief functions},
  author={Marie-H{\'e}l{\`e}ne Masson and Thierry Denoeux},
  journal={Pattern Recognition Letters},
The problem of clustering objects based on interval-valued dissimilarities is tackled in the framework of the Dempster-Shafer theory of belief functions. The proposed method assigns to each object a basic belief assignment (or mass function) defined on the set of clusters, in such a way that the belief and the plausibility that any two objects belong to the same cluster reflect, respectively, the observed lower and upper dissimilarity values. Experiments with synthetic and real data sets… CONTINUE READING


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Publications referenced by this paper.
Showing 1-10 of 19 references

Analysis of symbolic data

  • E. Diday, H. Bock
  • Exploratory methods for extracting statistical…
  • 2000
2 Excerpts

Criterion-based divisive clustering for symbolic objects

  • M. Chavent
  • H.H. Bock and E. Diday, editors, Analysis of…
  • 2000
1 Excerpt

Fuzzy models and algorithms for pattern recognition and image processing

  • J. C. Bezdek, J. Keller, R. Krishnapuram, N. R. Pal
  • Kluwer Academic Publishers, Boston
  • 1999

Clustering relational data containing noise and outliers

  • R. N. Davé
  • FUZZ’IEEE 98, pages 1411–1416, Anchorage
  • 1998
1 Excerpt

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