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

12 引文 (Scopus)

摘要

Spatio-temporal data mining involves extracting and analyzing useful information embedded in a large spatio-temporal database. Cluster analysis, one of the data mining techniques, provides the capability 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 determined from 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 transform 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 Hawaii International Conference on System Sciences
發行者IEEE
頁數1
ISBN(列印)0769504930
出版狀態Published - 2000 一月 1
事件The 33rd Annual Hawaii International Conference on System Siences (HICSS-33) - Maui, USA
持續時間: 2000 一月 42000 一月 7

出版系列

名字Proceedings of the Hawaii International Conference on System Sciences
ISSN(列印)1060-3425

Other

OtherThe 33rd Annual Hawaii International Conference on System Siences (HICSS-33)
城市Maui, USA
期間00-01-0400-01-07

指紋

Wavelet transforms
Data mining
Cluster analysis
Air
Discrete wavelet transforms
Self organizing maps
Neural networks

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

引用此文

Li, S-T., Chou, S. W., & Pan, J. J. (2000). Multi-resolution spatio-temporal data mining for the study of air pollutant regionalization. 於 Proceedings of the Hawaii International Conference on System Sciences (Proceedings of the Hawaii International Conference on System Sciences). IEEE.
Li, Sheng-Tun ; Chou, Shih Wei ; Pan, Jeng Jong. / Multi-resolution spatio-temporal data mining for the study of air pollutant regionalization. Proceedings of the Hawaii International Conference on System Sciences. IEEE, 2000. (Proceedings of the Hawaii International Conference on System Sciences).
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abstract = "Spatio-temporal data mining involves extracting and analyzing useful information embedded in a large spatio-temporal database. Cluster analysis, one of the data mining techniques, provides the capability 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 determined from 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 transform provide detailed time-variation features that can be used to detect the air pollutant spatial variation in a selected time period.",
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Li, S-T, Chou, SW & Pan, JJ 2000, Multi-resolution spatio-temporal data mining for the study of air pollutant regionalization. 於 Proceedings of the Hawaii International Conference on System Sciences. Proceedings of the Hawaii International Conference on System Sciences, IEEE, The 33rd Annual Hawaii International Conference on System Siences (HICSS-33), Maui, USA, 00-01-04.

Multi-resolution spatio-temporal data mining for the study of air pollutant regionalization. / Li, Sheng-Tun; Chou, Shih Wei; Pan, Jeng Jong.

Proceedings of the Hawaii International Conference on System Sciences. IEEE, 2000. (Proceedings of the Hawaii International Conference on System Sciences).

研究成果: Conference contribution

TY - GEN

T1 - Multi-resolution spatio-temporal data mining for the study of air pollutant regionalization

AU - Li, Sheng-Tun

AU - Chou, Shih Wei

AU - Pan, Jeng Jong

PY - 2000/1/1

Y1 - 2000/1/1

N2 - Spatio-temporal data mining involves extracting and analyzing useful information embedded in a large spatio-temporal database. Cluster analysis, one of the data mining techniques, provides the capability 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 determined from 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 transform provide detailed time-variation features that can be used to detect the air pollutant spatial variation in a selected time period.

AB - Spatio-temporal data mining involves extracting and analyzing useful information embedded in a large spatio-temporal database. Cluster analysis, one of the data mining techniques, provides the capability 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 determined from 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 transform provide detailed time-variation features that can be used to detect the air pollutant spatial variation in a selected time period.

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M3 - Conference contribution

AN - SCOPUS:0033889096

SN - 0769504930

T3 - Proceedings of the Hawaii International Conference on System Sciences

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PB - IEEE

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Li S-T, Chou SW, Pan JJ. Multi-resolution spatio-temporal data mining for the study of air pollutant regionalization. 於 Proceedings of the Hawaii International Conference on System Sciences. IEEE. 2000. (Proceedings of the Hawaii International Conference on System Sciences).