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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.
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Contributor : Yiru Zhang <>
Submitted on : Tuesday, September 25, 2018 - 2:00:31 PM
Last modification on : Friday, March 6, 2020 - 4:10:03 PM
Document(s) archivé(s) le : Wednesday, December 26, 2018 - 12:24:11 PM


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  • HAL Id : hal-01880400, version 1


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), BFAS, Sep 2018, Compiègne, France. ⟨hal-01880400⟩



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