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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
Open Access Research Article

DOFCM- PSO : A novel hybridized fuzzy clustering technique for segmentation of noisy mammogram images

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pp. 383–401Vol. 46Issue 2March 2025DOI: 10.47974/JIOS-1922XML
Received:
14 Nov 2024
Published Online:
17 Mar 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1922
Pages:
383–401

Abstract

Fuzzy clustering techniques are commonly applied to manage intricate data patterns using fuzzy partitioning methods. However, their performance deteriorates in presence of noise/outliers and higher-dimensional spaces. To address these limitations, we propose a hybrid algorithm DOFCM-PSO which hybridizes the Density-Oriented Fuzzy C Means clustering technique with metaheuristic algorithm, Particle Swarm Optimization to leverage their respective strengths. The proposed algorithm addresses the challenges of noisy datasets in higher dimensional spaces and provides a promising solution. We evaluate and compare the performance of DOFCM-PSO with existing algorithms on three benchmark datasets (Iris, Wine, and Glass), four real-world digital images, and three breast cancer image datasets. The experimental results demonstrate that DOFCM-PSO consistently outperforms all other algorithms. By effectively integrating DOFCM and PSO, our algorithm achieves improved accuracy, robustness, and stability in various applications. The findings highlight the potential of DOFCM-PSO as a valuable tool for real-world applications.

Keywords

Fuzzy ClusteringMetaheuristic AlgorithmsOptimization

Subject Classifications

Primary 93A30Secondary 49K15

References

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