2D hierarchical fuzzy clustering using kernel-based membership functions

2D clustering aims at solving problems concerning bi-dimensional datasets in several application fields, such as medical imaging, image retrieval, computer vision and so on. A novel approach for 2D hierarchical fuzzy clustering is proposed, which relies on the use of kernel-based membership functions. This new metric allows to obtain unconstrained structures for data modelling. The performed tests show that the proposed approach can overcome well-known hierarchical clustering algorithms against different benchmarks, also having the chance to be deployed on parallel computing architectures.