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In-situ exploration by spacecraft and planetary rovers will increasingly require knowledge "on demand" in the future as downlinlk constraints limit the amount of information that can be transmitted from these platforms back to Earth. Several on-board processing methods have the potential to significantly enhance scientific results in these settings. They include automatic detection of natural satellites of planetary bodies, investigation of possible surface motions on planets and planetary moons, and directed acquisition of scientific data by planetary rovers. The key ingpredient in all three cases is the need to process scientific data directly on-board, so that information can be rapidly provided to an automated spacecraft executive and/or to ground-based principal inesiators (pis). We discuss, herein, recent developments in data mining technology that were designed initially for ground-based scientific data analysis. We then outline how these ideas can be migrated to on-board platforms to dramatically enhance the scientific capabilities of autonomous spacecraft.