New dynamic clustering approaches within belief function framework

Hariz, Sarra Ben; Elouedi, Zied

doi:10.3233/ida-140648

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…Hariz & Elouedi (2014) BCDP: IKBKM and DKBKM [111] dynamic clustering based on the K-modes algorithm that uses the Transferable Belief Model (TBM) concepts [112] BKM [113] Cao et al ( 2017) k-mw-modes [68] clustering categorical matrix-object data based on the K-modes algorithm K-modes, Wk-modes [114], Cao [115], FCCM [116] Heloulou et al ( 2017) MOCSG [81] the multi-objective clustering based-sequential game theoretic that extends the ClusSMOG algorithm [117] K-modes, PAM [118], and single linkage algorithm [16] Salem et al ( 2018) MFk-M [64] frequency-based method to update the modes and the Manhattan distance metric to compute the distance K-modes, K-means…”

Section: Authors (Year) Algorithms Methods 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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