By: Timothy J Brown (Editor), Paul W Mielke (Editor)
80 pages, b/w illustrations
Statistical Mining and Data Visualization in Atmospheric Sciences brings together in one place important contributions and up-to-date research results in this fast moving area. Statistical Mining and Data Visualization in Atmospheric Sciences serves as an excellent reference, providing insight into some of the most challenging research issues in the field. Reprinted from Data Mining And Knowledge Discovery, 4:1
- Guest Editorial: Statistical Mining and Data Visualization in Atmospheric Sciences; T.J. Brown, P.W. Mielke, Jr.
- Euclidean Distance Based Permutation Methods in Atmospheric Science; P.W. Mielke, Jr., K.J. Berry.
- Bootstrapping to Assess and Improve Atmospheric Prediction Models; J.S. Rao.
- Using Linked Micromap Plots to Characterize Omernik Ecoregions; D.B. Carr, et al.
- Visual Data Mining in Atmospheric Science Data; M. Macedo, et al.
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