Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations

• Published in: Annals of operations research 2024-11, Vol.342 (3), p.1605-1628
• Main Authors: D’Urso, Pierpaolo, De Giovanni, Livia, Alaimo, Leonardo Salvatore, Mattera, Raffaele, Vitale, Vincenzina
• Format: Article
• Language: English
• Subjects: Business and Management; Cluster analysis; Clustering; Combinatorics; Data analysis; Entropy; Maximum entropy method; Operations research; Operations Research/Decision Theory; Original Research; Regularization; Statistical methods; Theory of Computation
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-023-05180-1

In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Following the partitioning around medoids fuzzy approach research line, a new fuzzy clustering model for interval-valued data is suggested. In particular, we propose a new model based on the use of the entropy as a regularization function in the fuzzy clustering criterion. The model uses a robust weighted dissimilarity measure to smooth noisy data and weigh the center and radius components of the interval-valued data, respectively. To show the good performances of the proposed clustering model, we provide a simulation study and an application to the clustering of scientific journals in research evaluation.