Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

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...

Full description

Saved in:
Bibliographic Details
Published in:Machine learning and knowledge extraction Vol. 6; no. 2; pp. 1009 - 1054
Main Authors: Cendana, Maya, Kuo, Ren-Jieh
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2024
Subjects:
Algorithms
bibliometric analysis
Bibliometrics
categorical data clustering
Cluster analysis
Clustering
Comparative analysis
Data mining
Data processing
Heuristic methods
K-modes algorithm
Methods
Noise sensitivity
Outliers (statistics)
Research design
Science
Taxonomy
taxonomy of clustering algorithm
Taiwan
ISSN:2504-4990, 2504-4990
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2504-4990
2504-4990
DOI:10.3390/make6020047