The main goal is to cluster subjects based on several distinct cross-correlation matrices.
So I have 4 correlation-matrices, each corresponding to dissimilarity $(1-r)$ of brain topography between subjects, (i.e. $s\times s$ cross-correlation matrix, $s =$ subjects), during a specific task (4) (i.e. $s\times s$ matrices for 4 tasks)
My first instinct would've been to perform hierarchical clustering on each matrix and then perform another clustering method on the resulting cluster memberships. However, i do feel that there's probably something better that can be done here.
I have around 1,000 subjects (so 1,000 subjects by 1,000 subjects matrix for each of the 4 tasks)
I could convert the 4 tasks in a 1D feature vector and then correlate between subjects but in doing so, i will lose a lot of information.
Thank you very much