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.

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.

2D hierarchical fuzzy clustering using kernel-based membership functions / Proietti, Andrea; Liparulo, Luca; Panella, Massimo. - In: ELECTRONICS LETTERS. - ISSN 0013-5194. - STAMPA. - 52:3(2016), pp. 193-195. [10.1049/el.2015.2602]

2D hierarchical fuzzy clustering using kernel-based membership functions

PROIETTI, ANDREA;LIPARULO, LUCA;PANELLA, Massimo
2016

Abstract

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.
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.
Computer architecture; computer vision; Fuzzy clustering; medical imaging; medical problems; membership functions; parallel architectures
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2D hierarchical fuzzy clustering using kernel-based membership functions / Proietti, Andrea; Liparulo, Luca; Panella, Massimo. - In: ELECTRONICS LETTERS. - ISSN 0013-5194. - STAMPA. - 52:3(2016), pp. 193-195. [10.1049/el.2015.2602]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/873295
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