Spatio-Temporal Data Mining for Typhoon Image Collection

  • Asanobu Kitamoto


Our research aims at discovering useful knowledge from the large collection of satellite images of typhoons using data mining approaches. We first introduce the creation of the typhoon image collection that consists of around 34,000 typhoon images for the northern and southern hemisphere, providing the medium-sized, richly-variational and quality-controlled data collection suitable for spatio-temporal data mining research. Next we apply several data mining approaches for this image collection. We start with spatial data mining, where principal component analysis is used for extracting basic components and reducing dimensionality, and it revealed that the major principal components describe latitudinal structures and spiral bands. Moreover, clustering procedures give the “birds-eye-view” visualization of typhoon cloud patterns. We then turn to temporal data mining, including state transition rules, but we demonstrate that it involves intrinsic difficulty associated with the nonlinear dynamics of the atmosphere, or chaos. Finally we briefly introduce our system IMET (Image Mining Environment for Typhoon analysis and prediction), which is designed for the intelligent and efficient searching and browsing of the typhoon image collection.

typhoon data mining spatio-temporal data mining typhoon image collection principal component analysis self-organizing map state transition rules 


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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Asanobu Kitamoto
    • 1
  1. 1.Research Center for Testbeds and PrototypingNational Institute of InformaticsTokyoJapan

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