In this paper, belief functions, defined on the lattice of 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 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 two data sets. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Masson, M. H., & Denoeux, T. (2009). Belief functions and cluster ensembles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5590 LNAI, pp. 323–334). https://doi.org/10.1007/978-3-642-02906-6_29
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