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.

Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations / D'Urso, Pierpaolo; De Giovanni, Livia; Alaimo, Ls; Mattera, R; Vitale, V. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - (In corso di stampa), pp. 1-24. [10.1007/s10479-023-05180-1]

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

D'Urso, P;De Giovanni, L
;
In corso di stampa

Abstract

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.
In corso di stampa
Fuzzy clustering, Robust clustering, Weighting system, Interval-valued data, Entropy, Journal citation indices, Evaluation
Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations / D'Urso, Pierpaolo; De Giovanni, Livia; Alaimo, Ls; Mattera, R; Vitale, V. - In: ANNALS OF OPERATIONS RESEARCH. - ISSN 0254-5330. - (In corso di stampa), pp. 1-24. [10.1007/s10479-023-05180-1]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11385/227438
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