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.
Sivieri, Katia (16235046500); de Oliveira, Sonia Mariza (58125873900); Marquez, Audrey de Souza (58125874000); Pérez-Jiménez, Jara (8568795800); Diniz, Susana Nogueira (57192692034)
Insights on β-glucan as a prebiotic coadjuvant in the treatment of diabetes mellitus: A review
2022
10.1016/j.fhfh.2022.100056
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148478807&doi=10.1016%2fj.fhfh.2022.100056&partnerID=40&md5=f9922bbaf9361260704a5c255da3f7af
All Open Access; Gold Open Access
Scopus
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