Automatic Clustering in Large Sets of Time Series

To study large sets of interacting time series, we combine spectral analysis of graph Laplacians with simulated annealing to automatically generate optimized clustering of time series, by minimization of cost functions characterizing clustering quality. We apply these techniques to evaluation of connectivity between cortex regions, via analysis of cortex activity recordings by sequences of 3-dimensional fMRI images.