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Data mining on high-dimensional heterogeneous data is a crucial component in information fusion application domains such as remote sensing, surveillance, and homeland security. The information processing requirements of these domains place a premium on security, robustness, performance, and sophisticated analytic methods. This paper introduces a database-centric approach that enables data mining and analysis of data that typically interest the information fusion community. The approach benefits from the inherent security, reliability, and scalability found in contemporary RDBMSs. The capabilities of this approach are demonstrated on satellite imagery. Hyperspectral data are mined using clustering (O-Cluster) and classification (Support Vector Machines) techniques. The data mining is performed inside the database, which ensures maintenance of data integrity and security throughout the analytic effort. Within the database, the clustering and classification results can be further combined with spatial processing components to enable additional analysis.