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.
|Title of host publication||Recent Advances in Soft Computing and Data Mining|
|Subtitle of host publication||Proceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022|
|Editors||Rozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris, Jemal H. Abawajy, Nureize Arbaiy|
|Number of pages||8|
|Publication status||Published - 4 May 2022|
|Name||Lecture Notes in Networks and Systems|