Artículo

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Resumen

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.
Cendana, Maya (57195409732); Kuo, Ren-Jieh (7004962398)
Categorical Data Clustering: A Bibliometric Analysis and Taxonomy
2024
10.3390/make6020047
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196779442&doi=10.3390%2fmake6020047&partnerID=40&md5=75f2a75e22f1aaf720150f42965ca859
Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei, 106, Taiwan
All Open Access; Gold Open Access
Scopus
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