Scientific Data Mining Task
- (Cho et al., 2008) ⇒ Yong Ju Cho, Naren Ramakrishnan, and Yang Cao. (2008). “Reconstructing Chemical Reaction Networks: Data Mining Meets System Identification.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401912
- (Mann et al., 2001) ⇒ Bob Mann, Roy Williams, Malcolm Atkinson, Ken Brodlie, Amos Storkey, and Chris Williams. (2001). “Scientific Data Mining, Integration, and Visualization." Report on the Workshop on Scientific Data Mining, Integration and Visualization (SDMIV).
- This report summarises the workshop on Scientific Data Mining, Integration and Visualization (SDMIV) held at the e-Science Institute, Edinburgh (eSI ) on 24-25 October 2002, and presents a set of recommendations arising from the discussion that took place there.
- (Grossman et al., 2001) ⇒ Robert L. Grossman, Chandrika Kamath, Philip Kegelmeyer, Vipin Kumar, and Raju R. Namburu, editors. (2001). “Data Mining for Scientific and Engineering Applications." Springer, Volume 2 of Massive Computing. ISBN:1402000332.
- Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.
- (Ramakrishanan & Grama, 2001) ⇒ Naren Ramakrishnan, and Ananth Grama. (2001). “Mining Scientific Data.” In: Advances in Computers, 55.
- 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 domains 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