Feature Mining Paradigms for Scientific Data [chapter]

Ming Jiang, Tat-Sang Choy, Sameep Mehta, Matt Coatney, Steve Barr, Kaden Hazzard, David Richie, Srinivasan Parthasarathy, Raghu Machiraju, David Thompson, John Wilkins, Boyd Gatlin
2003 Proceedings of the 2003 SIAM International Conference on Data Mining  
Numerical simulation is replacing experimentation as a means to gain insight into complex physical phenomena. Analyzing the data produced by such simulations is extremely challenging, given the enormous sizes of the datasets involved. In order to make efficient progress, analyzing such data must advance from current techniques that only visualize static images of the data, to novel techniques that can mine, track, and visualize the important features in the data. In this paper, we present our
more » ... search on a unified framework that addresses this critical challenge in two science domains: computational fluid dynamics and molecular dynamics. We offer a systematic approach to detect the significant features in both domains, characterize and track them, and formulate hypotheses with regard to their complex evolution. Our framework includes two paradigms for feature mining, and the choice of one over the other, for a given application, can be determined based on local or global influence of relevant features in the data.
doi:10.1137/1.9781611972733.2 dblp:conf/sdm/JiangCMCBHRPMTWG03 fatcat:vdyzq5ceqbf5tpao65anfvqsjy