Categorical fuzzy entropy c-means

Mahamadou, Abdoul Jalil Djiberou; Antoine, Violaine; Nguifo, Engelbert Mephu; Moreno, Sylvain

doi:10.1109/fuzz48607.2020.9177666

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Authors (Year) Algorithms Measurement-based Comparisons1

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Cited by 2 publications

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“…Zhang et al ( 2023) MAP, BFKMG [89] Bayesian dissimilarity measure to measure the dissimilarity, Kullback-Leibler (KL) divergence-based regularization to find the patterns in datasets Cao [125], FKMFC [150], KL-FCM-GM [214], MWK-DC [215], SBC-C, CFE [216], UDM…”

Section: Authors (Year) Algorithms Measurement-based Comparisonsmentioning

confidence: 99%

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

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