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. 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.