The past two decades have seen rapid advances in high performance computing and tools for data acquisition in a variety of scientific domains. Coupled with the availability of massive storage systems and fast networking technology to manage and assimilate data, these have given a significant impetus to data mining in the scientific domain. Data mining is now recognized as a key computational technology, supporting traditional analysis, visualization, and design tasks. Diverse applications in

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... ains such as mineral prospecting, computer aided design, bioinformatics, and computational steering are now being viewed in the data mining framework. This has led to a very effective crossfertilization of computational techniques from both continuous and discrete perspectives. In this chapter, we characterize the nature of scientific data mining activities and identify dominant recurring themes. We discuss algorithms, techniques, and methodologies for their effective application and present application studies that summarize the stateof-the-art in this emerging field. We conclude by identifying opportunities for future research in emerging domains.