Visual Data Mining In Atmospheric Science Data

摘要

This paper discusses the use of simple visual tools to explore multivariate spatially-referenced data. It describes interactive approaches such as linked brushing, and dynamic methods such as the grand tour, applied to studying the Comprehensive Ocean-Atmosphere Data Set (COADS). This visual approach provides an alternative way to gain understanding of high-dimensional data. It also provides cross-validation and visual adjuncts to the more computationally intensive data mining techniques.