Fuzzy Density-Based Clustering for Medical Diagnosis

Syed Muhammad Waqas, Kashif Hussain, Salama A. Mostafa, Nazri Mohd Nawi, Sumra Khan

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


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.
Original languageEnglish
Title of host publicationRecent Advances in Soft Computing and Data Mining
Subtitle of host publicationProceedings of the Fifth International Conference on Soft Computing and Data Mining (SCDM 2022), May 30-31, 2022
EditorsRozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris, Jemal H. Abawajy, Nureize Arbaiy
Number of pages8
ISBN (Electronic)978-3-031-00828-3
ISBN (Print)978-3-031-00827-6
Publication statusPublished - 4 May 2022

Publication series

NameLecture Notes in Networks and Systems

Cite this