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A COMPREHENSIVE EVALUATION OF ROUGH SETS CLUSTERING IN UNCERTAINTY DRIVEN CONTEXTS.
- Source :
- Studia Universitatis Babes-Bolyai, Informatica; Jun2024, Vol. 69 Issue 1, p41-56, 16p
- Publication Year :
- 2024
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ROUGH sets
CLUSTER set theory
MACHINE learning
SUPERVISED learning
Subjects
Details
- Language :
- English
- ISSN :
- 1224869X
- Volume :
- 69
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Studia Universitatis Babes-Bolyai, Informatica
- Publication Type :
- Academic Journal
- Accession number :
- 177889338
- Full Text :
- https://doi.org/10.24193/subbi.2024.1.03