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Ph.D. Thesis by Donald McCuan (advisor Andrew Knyazev), Department of Mathematical and Statistical Sciences, University of Colorado Denver, 2012, originally posted at http://math.ucdenver.edu/theses/McCuan_PhdThesis.pdf (1126)

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We develop novel algorithms and software on parallel computers for data clustering of large datasets. We are interested in applying our approach, e.g., for analysis of large datasets of microarrays or tiling arrays in molecular biology and for segmentation of high resolution images.

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Recent advances in functional connectivity (FC) analysis of functional magnetic resonance imaging (fMRI) data facilitate the characterization of the brain’s intrinsic functional networks (FC-fMRI). Because the fMRI signal does not provides a perfect representation of neuronal activity, the potential for FC-fMRI to identify functionally relevant networks critically depends upon separating overlapping signals from one another and from external noise. As a step in data preconditioning, researchers often band-pass filter fMRI signals to the range from 0.01 Hz to 0.1 Hz.

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