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

D’Urso, Pierpaolo; Giovanni, Livia De; Alaimo, Leonardo Salvatore; Mattera, Raffaele; Vitale, Vincenzina

doi:10.1007/s10479-023-05180-1

Cited by 8 publications

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In work [24], fuzzy entropy estimates are used to solve the supplier selection problem. Works [25] - [27] present the use of fuzzy entropy in classification and clustering problems.…”

Section: Possibility Theory and Hartley Measurementioning

confidence: 99%

Review of Methods for Classifying and Assessing Information Uncertainty

Uzhga-Rebrov,

Grabusts

2024

ETR

View full text Add to dashboard Cite

There is a large number and variety of uncertainties that relate to almost all areas of life and the activity of people. Based on numerous literature sources, this article classifies various uncertainties into the following large groups: (1) stochastic uncertainties and (2) numerical uncertainties, including fuzzy and possibilistic ones. The article presents general approaches to assessing uncertainties in each of these groups. In addition, the article presents the main characteristics of general uncertainty estimates and provides an illustrative example.

show abstract

“…In work [24], fuzzy entropy estimates are used to solve the supplier selection problem. Works [25] - [27] present the use of fuzzy entropy in classification and clustering problems.…”

Section: Possibility Theory and Hartley Measurementioning

confidence: 99%

Review of Methods for Classifying and Assessing Information Uncertainty

Uzhga-Rebrov,

Grabusts

2024

ETR

View full text Add to dashboard Cite

show abstract

Robust DTW-based entropy fuzzy clustering of time series

D’Urso,

De Giovanni,

Vitale

2023

Ann Oper Res

View full text Add to dashboard Cite

Time series are complex data objects whose partitioning into homogeneous groups is still a challenging task, especially in the presence of outliers or noisy data. To address the problem of robustness against outliers in clustering techniques, this paper proposes a robust fuzzy C-medoids method based on entropy regularization. In-depth, we use an appropriate exponential transformation of the dissimilarity based on Dynamic Time Warping, which can be computed also for time series of different length. In addition, the fuzzy framework provides the necessary flexibility to cope with the complexity of the features space. It allows a time series to be assigned to more than one group, considering potential switching behaviours. Moreover, the use of a medoids-based approach enables the identification of observed representative objects within the dataset, thus enhancing interpretability for practical applications. Through an extensive simulation study, we successfully demonstrate the effectiveness of our proposal, comparing and emphasizing its strengths. Finally, our proposed methodology is applied to the daily mean concentrations of three air pollutants in 2022 in the Province of Rome. This application highlights its potential, namely the capability to intercept outliers and switching time series while preserving group structures.

show abstract

OWA-based multi-criteria decision making based on fuzzy methods

D’Urso,

Chachi,

Kazemifard

et al. 2024

Ann Oper Res

View full text Add to dashboard Cite

One of the most important challenges in Multi-Attribute Decision Making (MADM) problems is how can the optimal weights of the criteria can be determined properly by the decision maker. In the relevant research literature, various methods based on the requirements and assumptions of the problem were introduced to determine the weights of the criteria. In this regard, in particular, the Yager’s OWA operator is one of the most significant and widely used approaches to evaluate the weights of criteria. But there is a drawback, that is, the results of Yager’s OWA operator depend only on the level of decision-maker’s risk and the number of the criteria. Therefore, in this paper, using a multi-objective decision making approach, we try to express this MADM challenge in the form of a generalization of the Yager’s OWA operators and Ahn’s method. One of the advantages of this generalization is that the proposed method uses all the information in the decision matrix compared to the methods proposed by Yager’s OWA operators and the Ahn’s method. The proposed approach is also able to enter various types of preferences considered by the decision maker for the criteria calculations as crisp or fuzzy quantities. Numerical examples and real dataset analysis based on a survey of students’ opinions on teaching activities are provided.

show abstract

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

Cited by 8 publications

References 45 publications

Review of Methods for Classifying and Assessing Information Uncertainty

Review of Methods for Classifying and Assessing Information Uncertainty

Robust DTW-based entropy fuzzy clustering of time series

OWA-based multi-criteria decision making based on fuzzy methods

Contact Info

Product

Resources

About