@ARTICLE { Rsds2281,
author = {Pizzi, N. and Ramanna, Sheela and Shan, S. and Suraj, Zbigniew and Peters, James F.}, 
title = {Classification of meteorological volumetric radar data using rough set methods}, 
journal = {Pattern Recognition Letters}, 
number = 	{(no. 6)}, 
volume = 24, 
pages = {911-920}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
month = {March}, 
year = 2003, 
issn = {0167-8655}, 
abstract = {This paper reports on a rough set approach to classifying meteorological volumetric radar data used to detect storm events responsible for summer severe weather. The classification of storm cells is a difficult problem due to the complex evolution of storm cells, the high dimensionality of the weather data, and the imprecision and incompleteness of the data. A rough set approach is used to classify different types of meteorological storm events. A considerable of different classification strategies techniques have been considered and compared to determine which approach will best classify the volumetric storm cell data coming from the Radar Decision Support System database of Environment Canada. The criterion for comparison is the accuracy coefficient in the classification over a testing data. The contribution of this paper is a new application of rough set theory in classifying meteorological radar data.}, 
keywords = {classification, data mining, decision classes, meteorological volumetric radar data, pattern recognition, }, 
}