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Article Dans Une Revue Array Année : 2020

Combining clusterings in the belief function framework

Résumé

In this paper, we propose a clustering ensemble method based on Dempster-Shafer Theory. In the first step, base partitions are generated by evidential clustering algorithms such as the evidential c-means or EVCLUS. Base credal partitions are then converted to their relational representations, which are combined by averaging. The combined relational representation is then made transitive using the theory of intuitionistic fuzzy relations. Finally, the consensus solution is obtained by minimizing an error function. Experiments with simulated and real datasets show the good performances of this method.
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Dates et versions

hal-02471590 , version 1 (08-02-2020)

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Feng Li, Shoumei Li, Thierry Denoeux. Combining clusterings in the belief function framework. Array, 2020, 6, pp.100018. ⟨10.1016/j.array.2020.100018⟩. ⟨hal-02471590⟩
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