Comparison of Validity Indexes for Fuzzy Clusters of fMRI Data

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.