An empirical study to determine the optimal k in Ek-NNclus method

Abstract : Ek-NNclus is a clustering algorithm based on the evidential k-nearest-neighbor rule. It has the advantage that the number of clusters can be detected unlike a c-means for example. However, the parameter k has crucial influence on the clustering results, especially for the number of clusters and clustering quality. Thus, the determination of k is an important issue to optimize the use of the Ek-NNclus algorithm. The authors of Ek-NNclus only give a large interval of k, which is not precise enough for real applications. In traditional clustering algorithms such as c-means and c-medoïd, the determination of c is a real issue and some methods have been proposed in the literature and proved to be efficient. In this paper, we borrow some methods from c determination solutions and propose a k determination strategy based on an empirical study.
Type de document :
Communication dans un congrès
5th International Conference on Beleif Functions (BELIEF2018), Sep 2018, Compiègne, France. 2018
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https://hal.archives-ouvertes.fr/hal-01880400
Contributeur : Yiru Zhang <>
Soumis le : mardi 25 septembre 2018 - 14:00:31
Dernière modification le : vendredi 30 novembre 2018 - 08:58:01

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  • HAL Id : hal-01880400, version 1

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Yiru Zhang, Tassadit Bouadi, Arnaud Martin. An empirical study to determine the optimal k in Ek-NNclus method. 5th International Conference on Beleif Functions (BELIEF2018), Sep 2018, Compiègne, France. 2018. 〈hal-01880400〉

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