Extracting activated regions of fMRI data using unsupervised learning
Heydar Davoudi, Ali Taalimi, Emad Fatemizadeh · 2009
Clustering approaches are going to efficiently define the activated regions of the brain in fMRI studies. However, choosing appropriate clustering algorithms and defining optimal number of clusters are still key problems of these methods. In this paper, we apply an improved version of Growing Neural Gas algorithm, which automatically operates on the optimal number of clusters. The decision criterion for creating new clusters at the heart of this online clustering is taken from MB cluster validity index. Comparison with other so-called clustering methods for fMRI data analysis shows that the proposed algorithm outperforms them in both artificial and real datasets.