In computational neuroimaging, the analysis of functional Magnetic Resonance Images (fMRIs) using fuzzy clustering methods is a promising data driven approach to explore brain functional connectivity. In this complex domain, accurate evaluation procedures based on suitable indexes, able to identify optimal clustering results, are of great values strongly affecting the validity and interpretation of the overall fMRI data analysis. A large number of clustering validation indexes have been proposed in literature. This work proposes a comparison analysis of eight representative fuzzy and crisp clustering validation indexes. Salient aspects of the proposed strategy are the use of the widely adopted fuzzy c-means algorithm as underlying fuzzy clustering algorithm and the use of resting state fMRI data from the NITRC repository.
Comparison of Validity Indexes for Fuzzy Clusters of fMRI Data
Vergani A. A.;
2019-01-01
Abstract
In computational neuroimaging, the analysis of functional Magnetic Resonance Images (fMRIs) using fuzzy clustering methods is a promising data driven approach to explore brain functional connectivity. In this complex domain, accurate evaluation procedures based on suitable indexes, able to identify optimal clustering results, are of great values strongly affecting the validity and interpretation of the overall fMRI data analysis. A large number of clustering validation indexes have been proposed in literature. This work proposes a comparison analysis of eight representative fuzzy and crisp clustering validation indexes. Salient aspects of the proposed strategy are the use of the widely adopted fuzzy c-means algorithm as underlying fuzzy clustering algorithm and the use of resting state fMRI data from the NITRC repository.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
