Open Access2D hierarchical fuzzy clustering using kernel‐based membership functions
Author(s) -
Proietti A.,
Liparulo L.,
Panella M.
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.2602
Subject(s) - cluster analysis , fuzzy clustering , hierarchical clustering , computer science , data mining , hierarchical clustering of networks , kernel (algebra) , artificial intelligence , fuzzy logic , pattern recognition (psychology) , correlation clustering , cure data clustering algorithm , mathematics , combinatorics
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.