首頁 > 網路資源 > 大同大學數位論文系統

Title page for etd-0213108-125733


URN etd-0213108-125733 Statistics This thesis had been viewed 1973 times. Download 10 times.
Author Li-Jen Kao
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2007 Semester 1
Degree Ph.D. Type of Document Doctoral Dissertation
Language English Page Count 85
Title Predicting Ocean Salinity and Temperature Variations Using Data Mining and Fuzzy Inference
Keyword
  • fuzzy inference
  • ocean salinity
  • ocean temperature
  • data mining
  • inter-transaction association rules
  • inter-transaction association rules
  • data mining
  • ocean temperature
  • ocean salinity
  • fuzzy inference
  • Abstract 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.
    Advisor Committee
  • Yo-Ping Huang - advisor
  • Liang-Teh Lee - co-chair
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • Shuenn-Shyang Wang - co-chair
  • Yo-Ping Huang - co-chair
  • Files indicate in-campus access at 10 years and off-campus not accessible
    Date of Defense 2008-01-25 Date of Submission 2008-02-13


    Browse | Search All Available ETDs