Abstract
Clustering is an effective technique for identifying patterns and structures in labeled and unlabeled datasets in the medical sector. Density-based clustering is a sophisticated machine learning technique for identifying distinctive patterns in large datasets. However, this approach has certain drawbacks like inability to determine local densities and overlapping clusters or clusters with blur boundaries. This paper embeds fuzzy logic with density-based clustering, for improved clustering separability. In order to validate the usability of the proposed approach, we use five real-world datasets belonging to medical domain.
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Acknowledgments
This research was supported by Universiti Tun Hussein Onn Malaysia (UTHM) through Tier 1 vot. H938.
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Waqas, S.M., Hussain, K., Mostafa, S.A., Nawi, N., Khan, S. (2022). Fuzzy Density-Based Clustering for Medical Diagnosis. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_26
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DOI: https://doi.org/10.1007/978-3-031-00828-3_26
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