Have say 1000 items and their pairwise correlations with each other, so 499,000 correlations. Want to use this correlation data to categorise the 1000 items into a limited number of categories that is set up front so say into 10 categories. Categories should work so that items in same category have relatively high correlation. What is a good way to approach/solve this problem?
There are many clustering algorithms that can be used with correlation distance.
In particular, try hierarchical clustering, DBSCAN, and OPTICS.