博士 / 大同大學 / 資訊工程學系(所) / 96 / Global ocean salinity and temperature variations are attracting increasing attention, due to its influence on global climate change. This research presents an efficient technique for analyzing Argo ocean data comprising time series of salinity and temperature measurements where informative salinity and temperature patterns are extracted. Most traditional mining techniques focus on finding associations among items within one transaction and are therefore unable to discover rich contextual patterns related to location and time. In order to show the associated salinity and temperature variations among different locations and time intervals, for example, “if the salinity rose from 0.15psu to 0.25psu in the area that is in the east-northeast direction and is near Taiwan, then the temperature will rise from 0℃ to 1.2℃ in the area that is in the east-northeast direction and is far away from Taiwan next month”, the research designs a transformation method to convert Argo spatial-temporal data to market-basket type data and then a quantitative inter-transaction association rules mining algorithm is proposed to apply to the transformed data set to get salinity and temperature variation patterns. The FITI and the PrefixSpan algorithms are adopted to maximize the mining efficiency. Next, a fuzzy inference model that employs the discovered salinity and temperature patterns as its rule base is designed to predict salinity and temperature variations. The strategy is applied to ocean salinity and temperature measurements obtained from the waters surrounding Taiwan. These experimental evaluations show that the proposed algorithm achieves better performance than other inter-transaction association rule mining algorithms.
| Identifer | oai:union.ndltd.org:TW/096TTU05392007 |
| Date | January 2008 |
| Creators | Li-Jen Kao, 高立仁 |
| Contributors | Yo-Ping Huang, 黃有評 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 85 |
Page generated in 0.0154 seconds