A correlation-based fuzzy cluster validity index with secondary options detector
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Wiroonsri, N.; Preedasawakul, O.
Publisher: Elsevier
Publication year: 2026
Volume number: 523
Start page: 109632
ISSN: 0165-0114
eISSN: 1872-6801
Languages: English-Great Britain (EN-GB)
Abstract
The optimal number of clusters is one of the main concerns when applying cluster analysis. Several cluster validity indexes have been introduced to address this problem. However, in some situations, there is more than one option that can be chosen as the final number of clusters. This aspect has been overlooked by most of the existing works in this area. In this study, a correlation-based fuzzy cluster validity index known as the Wiroonsri–Preedasawakul (WP) index is introduced. This index is defined based on the correlation between the actual distance between a pair of data points and the distance between adjusted centroids with respect to that pair. The performance of our index is then evaluated and compared with several existing indexes, including Xie–Beni, Pakhira–Bandyopadhyay–Maulik, Tang, Wu–Li, generalized C, and Kwon2. The evaluation is conducted on four types of datasets: artificial datasets, real-world datasets, simulated datasets with second option, and image datasets, using the fuzzy c-means algorithm. Overall, the WP index outperforms most, if not all, of these indexes in terms of accurately detecting the optimal number of clusters and providing accurate secondary options. Moreover, our index remains effective even when the fuzziness parameter m is set to a large value. Our R package called UniversalCVI used in this work is available at https://CRAN.R-project.org/package=UniversalCVI. © 2025 Elsevier B.V.
Keywords
No matching items found.
Contact Information