Data clustering as an emergent consensus of autonomous agents
Applicationes Mathematicae
MSC: Primary 94A08; Secondary 91D30, 68P05
DOI: 10.4064/am2507-2-2025
Published online: 19 May 2025
Abstract
We present a data segmentation method based on a first-order density-induced consensus protocol. We provide a mathematically rigorous analysis of the consensus model leading to stopping criteria of the data segmentation algorithm. To illustrate our method, the algorithm is applied to two-dimensional shape datasets and selected images from Berkeley Segmentation Dataset. The method can be seen as an augmentation of classical clustering techniques for multimodal feature space, such as DBSCAN. It showcases a curious connection between data clustering and collective behavior.
