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 4 → 2000 一月 7 |
出版系列
| 名字 | Proceedings of the Hawaii International Conference on System Sciences |
|---|---|
| ISSN（列印） | 1060-3425 |
Other
| Other | The 33rd Annual Hawaii International Conference on System Siences (HICSS-33) |
|---|---|
| 城市 | Maui, USA |
| 期間 | 00-01-04 → 00-01-07 |
指紋
All Science Journal Classification (ASJC) codes
- Computer Science(all)
引用此文
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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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UR - http://www.scopus.com/inward/citedby.url?scp=0033889096&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0033889096
SN - 0769504930
T3 - Proceedings of the Hawaii International Conference on System Sciences
BT - Proceedings of the Hawaii International Conference on System Sciences
PB - IEEE
ER -