A Comprehensive Evaluation of Rough Sets Clustering in Uncertainty Driven Contexts

This paper presents a comprehensive evaluation of the Agent BAsed Rough sets Clustering (ABARC) algorithm, an approach using rough sets theory for clustering in environments characterized by uncertainty. Several experiments utilizing standard datasets are performed in order to compare ABARC against...

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Bibliographic Details
Published inStudia Universitatis Babes-Bolyai: Series Informatica Vol. 69; no. 1
Main Author Arnold SZEDERJESI-DRAGOMIR
Format Journal Article
LanguageEnglish
Published Babes-Bolyai University, Cluj-Napoca 10.06.2024
Subjects
clustering
metrics
rough sets
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Summary:This paper presents a comprehensive evaluation of the Agent BAsed Rough sets Clustering (ABARC) algorithm, an approach using rough sets theory for clustering in environments characterized by uncertainty. Several experiments utilizing standard datasets are performed in order to compare ABARC against a range of supervised and unsupervised learning algorithms. This comparison considers various internal and external performance measures to evaluate the quality of clustering. The results highlight the ABARC algorithm’s capability to effectively manage vague data and outliers, showcasing its advantage in handling uncertainty in data. Furthermore, they also emphasize the importance of choosing appropriate performance metrics, especially when evaluating clustering algorithms in scenarios with unclear or inconsistent data. Received by the editors: 5 March 2024. 2010 Mathematics Subject Classification. 68T37, 68T99. 1998 CR Categories and Descriptors. I.5.3 Pattern Recognition.: Clustering – Algorithms; I.5.3 Pattern Recognition.: Clustering – Similarity measures.
ISSN:2065-9601
DOI:10.24193/subbi.2024.1.03