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Activity Number: 157
Type: Topic Contributed
Date/Time: Monday, August 7, 2006 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Graphics
Abstract - #306172
Title: Using Data Mining Tools in the Study of NASA Ozone Data
Author(s): Wei-hong Wang*+ and Pin-Shuo Liu
Companies: The College of New Jersey and William Paterson University
Address: Math Department, Ewing, NJ, 08628-4700,
Keywords: data displays ; spatial data & GIS ; temporal data ; neural network ; predictive modeling ; genetic algorithm
Abstract:

This paper investigates the ozone data as presented in the 2006 ASA Data Exposition (www.amstat-online.org/sections/graphics/dataexpo/2006.php). The data contains eight variables in 506 files and was collected on 24x24 geographical grids over 72 months. Our investigation treats ozone as the target variable and focuses on the spatial and temporal behaviors of the target and predictors. The study also compares the predictive powers of various data mining tools in the following two areas: regression, neural network, boosted trees, random forest, support vector machines, genetic algorithm, and MARSplines for cross-sectional data and neural network and temporal system-equation model for time series data. The paper will discuss a variety of ways in the comparison of the models and in the dynamic displays of spatial and temporal data.

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