# Correlation-Matrix Based Hierarchical Clustering On Several Matrices

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