Multi-resolution spatio-temporal data mining for the study of air pollutant regionalization

Sheng Tun Li, Shih Wei Chou, Jeng Jong Pan

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Spatio-temporal data mining involves exti-acting and analyzing useful information embedded in a large spatio - temporal database. Cluster analysis, one of the data mining techniques, provides the capabilip to investigate the spatio-temporal variation of data. Previous studies in cluster analysis indicate that the optimal number of clusters could be varied with the temporal scale of input data. This study employs multi-scale wavelet transforms and self-organizing map neural networks to mine air pollutant data. Experimental results show that regions determinedfrom wavelet transform approach can reduce the local small regions using a small scale input data and improve the over-smoothed regions using one large scale input data. The results of cluster analysis using data generated from discrete wavelet transform and continuous wavelet transform also discussed in this paper. Data generated from continuous wavelet transfovm provide detailed time-variation features that can be used to detect the air pollutant spatial variation in a selected time period.

原文English
主出版物標題Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, HICSS 2000
發行者IEEE Computer Society
ISBN(電子)0769504930
出版狀態Published - 2000
事件33rd Annual Hawaii International Conference on System Sciences, HICSS 2000 - Maui, United States
持續時間: 2000 一月 42000 一月 7

出版系列

名字Proceedings of the Annual Hawaii International Conference on System Sciences
2000-January
ISSN(列印)1530-1605

Conference

Conference33rd Annual Hawaii International Conference on System Sciences, HICSS 2000
國家/地區United States
城市Maui
期間00-01-0400-01-07

All Science Journal Classification (ASJC) codes

  • 工程 (全部)

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