551	RWTH Publication No: 811917        2020
TITLE	Mean field models for large data-clustering problems
AUTHORS	Michael Herty, Lorenzo Pareschi, Giuseppe Visconti
ABSTRACT	We consider mean-field models for data--clustering problems starting from a generalization of the bounded confidence model for opinion dynamics. The microscopic model includes information on the position as well as on additional features of the particles in order to develop specific clustering effects. The corresponding mean--field limit is derived and properties of the model are investigated analytically. In particular, the mean--field formulation allows the use of a random subsets algorithm for efficient computations of the clusters. Applications to shape detection and image segmentation on standard test images are presented and discussed.
KEYWORDS	Data clustering, opinion dynamic, mean field equations, image segmentation, shape detection
DOI	10.3934/nhm.2020027
PUBLICATION	Networks and Heterogeneous Media 2020, Volume 15, Issue 3: 463-487