Belief Hierarchical Clustering

Abstract : In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool.
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Communication dans un congrès
3rd International Conference on Belief Functions, Sep 2014, Oxford, United Kingdom. pp.68 - 76, 2014, <10.1007/978-3-319-11191-9_8>
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https://hal.archives-ouvertes.fr/hal-01102028
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Dernière modification le : mercredi 2 août 2017 - 10:06:45
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Wiem Maalel, Kuang Zhou, Arnaud Martin, Zied Elouedi. Belief Hierarchical Clustering. 3rd International Conference on Belief Functions, Sep 2014, Oxford, United Kingdom. pp.68 - 76, 2014, <10.1007/978-3-319-11191-9_8>. <hal-01102028>

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