Developing an automatic fuzzy clustering algorithm for point data based on the circle similarity and applying to images
Nghiep Le-Dai, Thanh Thai-Van, Tai Vovan · Statistics · 2025
This study proposes a novel algorithm to cluster for point data with significant improvements. First, each point is assigned to a circle with an appropriate radius, from which a new measure for their similarity is proposed. This distance is then used to determine the appropriate number of clusters. Once the number of clusters is determined, the steps to establish the fuzzy relationship of each element with the clusters using the proposed distance are detailed. The proposed algorithm is thoroughly detailed in each implementation step, numerical examples are illustrated, and it can be executed using an established MATLAB procedure. A key motivation for this algorithm is its application to image data, where characteristics are represented as points for recognition. Applications on various image datasets with different characteristics demonstrate that the proposed algorithm is stable and yields good results, outperforming many popular algorithms as well as recently proposed algorithms.