There is an urgent need to advance and apply quantitative and qualitative approaches to the study of epilepsy and brain disorders. As uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost, this project is aimed at the development of an automated seizure prediction system and brain abnormal activity classifier. To achieve this goal, Drs. Novotny and Ojemann, in collaboration with Dr. Chaovalitwongse in the UW Dept. of Engineering, are developing optimization-based data mining (DM) approaches to quantitatively analyze the brain activity through electroencephalogram (EEG) data. The proposed DM techniques will excavate hidden patterns/relationships in EEGs, which will give a greater understanding of brain functions (as well as other complex systems) from a system perspective. Specifically, a new DM paradigm for the seizure prediction and brain activity classification will be developed based on novel optimization-based DM techniques for feature selection, clustering, and classification. The proposed research will contribute to the computer science, engineering and medical communities along the following four lines: (1) the development of novel mathematical models and optimization techniques for DM problems and time series analysis, (2) the implementation of statistical techniques to detect patterns from selected features/clusters for predicting seizures and classifying normal and epileptic EEG activity, (3) the utility of detection theory and the experimental designs to assess and validate the efficacy, robustness, and uncertainty of the proposed DM paradigm as well as fine-tune the optimal parameter setting, (4) the extension of the fundamental research findings in optimization and DM to other cross-disciplinary research, which will constitute a new avenue of research in optimization-based DM and time series analysis.