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Ensemble clustering in the belief functions framework

Abstract : In this paper, belief functions, defined on the lattice of intervals partitions of a set of objects, are investigated as a suitable framework for combining multiple clusterings. We first show how to represent clustering results as masses of evidence allocated to sets of partitions. Then a consensus belief function is obtained using a suitable combination rule. Tools for synthesizing the results are also proposed. The approach is illustrated using synthetic and real data sets.
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Submitted on : Tuesday, December 13, 2011 - 2:44:57 PM
Last modification on : Monday, September 5, 2022 - 4:22:29 PM
Long-term archiving on: : Wednesday, March 14, 2012 - 2:22:44 AM


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  • HAL Id : hal-00651375, version 1



Marie-Hélène Masson, Thierry Denoeux. Ensemble clustering in the belief functions framework. International Journal of Approximate Reasoning, Elsevier, 2011, 52 (1), pp.92-109. ⟨hal-00651375⟩



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