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.
De Iuliis, Melissa (57204807865); Cardoni, Alessandro (57200816744); Paolo Cimellaro, Gian (58916210700)
Resilience and safety of civil engineering systems and communities: A bibliometric analysis for mapping the state-of-the-art
2024
10.1016/j.ssci.2024.106470
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186519876&doi=10.1016%2fj.ssci.2024.106470&partnerID=40&md5=9bd8cb41d13f5a4026706ab508301d02
Dept. of Structural and Geotechnical Engineering, Sapienza University of Rome, Via Eudossiana, 18, Rome, Italy; Dept. of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, Torino, Italy
All Open Access; Hybrid Gold Open Access
Scopus
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