Credal-based fuzzy number data clustering

Liu, Zhe

doi:10.1007/s41066-023-00410-0

Cited by 25 publications

(4 citation statements)

References 53 publications

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“…Fuzzy sets, originally proposed by Zadeh [6], provide an effective mathematical framework for processing uncertainty information. Since then, fuzzy sets have gained much attention from researchers and applied to different fields [7,8,9]. After fuzzy sets, Atanassov [10] introduced the notion of intuitionistic fuzzy sets (IFSs), which encompass both the degrees of membership and non-membership, offering a richer portrayal of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A Distance Measure of Fermatean Fuzzy Sets Based on Triangular Divergence and its Application in Medical Diagnosis

Liu

2024

J. Oper. Intell.

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Fermatean fuzzy sets (FFSs), as one of the representative variants of fuzzy sets, have broad application prospects. FFSs have advantages in modeling uncertain information and therefore have been widely applied. However, how to perfectly quantify the differences between FFS remains an open question. This paper introduces a new distance measure for FFSs, utilizing triangular divergence. The proposed measure is designed to rectify the limitations in the current measure, offering a more effective solution for analyzing FFSs. Moreover, we demonstrate that the proposed distance measure satisfies some basic properties and further show its effectiveness through several numerical examples. Finally, we explore the performance of the proposed distance measure in a medical diagnosis application, and the results show that the proposed distance measure can well overcome the limitations of the current measure.

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Section: Introductionmentioning

confidence: 99%

A Distance Measure of Fermatean Fuzzy Sets Based on Triangular Divergence and its Application in Medical Diagnosis

Liu

2024

J. Oper. Intell.

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“…However, there is uncertainty and incompleteness of data (Liu & Letchmunan, 2024) during fuzzy clustering. Liu (2023) proposed the CFNDC method to reduce the risk associated with uncertainty, while Liu and Letchmunan (2023) proposed the DST method to optimize the clustering process for incomplete instances.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Regional Co-Location Pattern Mining Based on Efficient Density Peak Clustering and Maximal Fuzzy Grid Cliques

Zhou,

Wang,

Wang

et al. 2024

JDSIS

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Due to the heterogeneity of data distribution in real life and the spatial autocorrelation among spatial instances, traditional spatial co-location pattern mining methods tend to ignore valuable information specific to local regions. To address the limitation, regional co-location pattern mining has been proposed to find patterns that may be hidden within local regions. In this paper, a fuzzy regional co-location pattern mining framework based on efficient density peak clustering and maximal fuzzy grid cliques is presented. By incorporating a grid-splitting method and fuzzy theory, an efficient density peak clustering algorithm is proposed to divide the global area into distinct local regions. Furthermore, we propose a method to materialize the neighbor relationships between instances based on the maximal fuzzy grid cliques, and parallelize the clustering process to improve the algorithm efficiency. Experimental results show that the proposed algorithm can not only reduce the time consumption by about 40%, but also mine meaningful patterns with tighter instance distributions.

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“…Among them, Dempster-Shafer evidence theory (DSET) [20], as a powerful tool for modeling uncertain and imprecise information, which has been successfully applied in pattern recognition [21][22][23], fault diagnosis [24,25], information fusion [26,27] and image analysis [28,29]. DSET provides a richer representation of uncertainty by allowing belief values to be assigned to sets of propositions, rather than just individual events [15].…”

Section: Introductionmentioning

confidence: 99%

A Belief Similarity Measure for Dempster-Shafer Evidence Theory and Application in Decision Making

Liu

2024

J. Soft. Comput. Decis. Anal.

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How to effectively deal with uncertain and imprecise information in decision making is a complex task. Dempster-Shafer evidence theory (DSET) is widely used for handling such challenges due to its ability to model uncertainty and imprecision. However, Dempster's rule can sometimes yield counterintuitive results when dealing with highly conflicting evidence. In this paper, we introduce a novel belief sine similarity measure, called $BS^2M$, which effectively measures the discrepancy between different pieces of evidence. We also establish that $BS^2M$ possesses important properties such as boundedness, symmetry, and non-degeneracy. Building upon $BS^2M$, we present a new method for decision making. The proposed method considers both the credibility and the information volume of each evidence, providing a more comprehensive reflection of their importance. To validate our method, we conduct experiment in target recognition application, demonstrating the effectiveness and rationality of the proposed method.

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Credal-based fuzzy number data clustering

Cited by 25 publications

References 53 publications

A Distance Measure of Fermatean Fuzzy Sets Based on Triangular Divergence and its Application in Medical Diagnosis

A Distance Measure of Fermatean Fuzzy Sets Based on Triangular Divergence and its Application in Medical Diagnosis

Fuzzy Regional Co-Location Pattern Mining Based on Efficient Density Peak Clustering and Maximal Fuzzy Grid Cliques

A Belief Similarity Measure for Dempster-Shafer Evidence Theory and Application in Decision Making

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