Elements of machine learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Self-organizing maps
Typhoon Analysis and Data Mining with Kernel Methods
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Evolution Map: Modeling State Transition of Typhoon Image Sequences by Spatio-Temporal Clustering
DS '02 Proceedings of the 5th International Conference on Discovery Science
A Problem Oriented Approach to Data Mining in Distributed Spatio-temporal Database
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
An intelligent typhoon damage prediction system from aerial photographs
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
High-dimensional shared nearest neighbor clustering algorithm
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Exploring multivariate spatio-temporal change in climate data using image analysis techniques
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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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.