Comparative Study of Two Clustering Algorithms: Performance Analysis of a New Algorithm Against the Evidential C-Means Algorithm

Clustering techniques are essential elements for exploring and analyzing data. This paper provides a comparative study between a new clustering algorithm based on a new density peak detection approach introducing the imprecise data concept, named Robust density peak detection with imprecision data (RDPTI), and the Evidential C-Means method [1–4]. The aim of this research is to analyze the performance, efficiency and usefulness of the new algorithm against the established Evidential C-Means method. Unsupervised statistical classification methods based on probability density function estimation have a wide field of application. In this paper, we propose a new algorithm based on density peaks. By introducing the notion of imprecise data and combining two noise detection methods, this proposed algorithm produces three types of clusters: singleton clusters, meta-clusters and outlier cluster. In order to demonstrate the effectiveness and robustness of the RDPTI method, artificial and real data are tested and the algorithm is compared with the Evidential C-means algorithm, which is a clustering algorithm based on the belief function theory. Experimental results show that the proposed algorithm RDPTI improves clustering accuracy over the Evidential C-Means method. The outcomes provide precious information for scientists looking to take advantage of new clustering techniques for a variety of applications in data analysis.