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

Yissam Lakhdar () and Khawla Bendadi
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Yissam Lakhdar: Regional Center for Education and Training Professions
Khawla Bendadi: École Marocaine des Sciences de L’ingénieur, EMSI

A chapter in Information Systems and Technological Advances for Sustainable Development, 2024, pp 319-327 from Springer

Abstract: Abstract 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.

Keywords: Density peak detection; Kernel density estimation; Imprecise data; Evidential C-Means; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-75329-9_35

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