In this thesis we develop data mining techniques to analyze sleep irregularities in humans. We investigate the effects of several demographic, behavioral and emotional factors on sleep progression and on patient's susceptibility to sleep-related and other disorders. Mining is performed over subjective and objective data collected from patients visiting the UMass Medical Center and the Day Kimball Hospital for treatment. Subjective data are obtained from patient responses to questions posed in a sleep questionnaire. Objective data comprise observations and clinical measurements recorded by sleep technicians using a suite of instruments together called polysomnogram. We create suitable filters to capture significant events within sleep epochs. We propose and employ a Window-based Association Rule Mining Algorithm to discover associations among sleep progression, pathology, demographics and other factors. This algorithm is a modified and extended version of the Set-and-Sequences Association Rule Mining Algorithm developed at WPI to support the mining of association rules from complex data types. We analyze both the medical as well as the statistical significance of the associations discovered by our algorithm. We also develop predictive classification models using logistic regression and compare the results with those obtained through association rule mining.