Employing Aggregations of Fuzzy Equivalences in Clustering and Visualization of Medical Data Sets
Piotr Lasek, Wojciech Rząsa, Anna Król · Procedia Computer Science · 2024
Data clustering algorithms are vital in artificial intelligence and data mining to uncover unknown patterns in data sets. This paper investigates the use of data clustering and visualization techniques to analyze medical data sets, with a focus on the application of fuzzy equivalence in clustering. Fuzzy equivalences provide a substitute for conventional distance metrics, improving the clustering procedure by allowing the selection of the optimal mix of fuzzy distance and aggregation for the data set under analysis. We explain how these equivalences are incorporated into clustering algorithms and their impact on visualizing medical data. Our approach determines the most suitable fuzzy equivalence and aggregation methods for data sets, based on the silhouette coefficient, and explores visualization scenarios to identify distinctive data set attributes. Hence, the incorporation of fuzzy equivalences and aggregations into clustering algorithms enables more adaptable tuning of clustering results based on the silhouette coefficient, and the suggested visualization facilitates a quick visual assessment of important data set characteristics.