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  • 學位論文

提高關鍵圖圖形在決策應用中的可讀性-調整關鍵字頻率與區別關鍵字層次

By Adjusting Keyword Frequency and Separating Keyword into Different Layers to Improve Readability of KeyGraph Diagram in Decision Making

指導教授 : 許芳誠

摘要


關鍵圖圖形不易被了解,一直是關鍵圖相關研究中的重要問題。透過前置處理方法的處理,提供一個適切的文件給關鍵圖當作投入,有助於關鍵圖產生一個更容易判讀與分析的關鍵圖圖形。而一個可讀性較高的關鍵圖圖形,可以讓決策者更清楚地瞭解現狀與決策情境。因此,在本研究中,提出兩種前置處理程序-詞頻修正與VFT分層前置處理程序,希望能藉由將這兩種前置處理程序,加入到原本關鍵圖既有的一般前置處理方法中,可以使得欲投入到關鍵圖中的文件得以更適合,使產生的關鍵圖圖形,可以更容易被觀圖者或決策者所瞭解,甚至從中發現決策機會。   為驗證本研究所提方法之可行性,研究將以個案研究的方式進行探索性分析,而在研究中以即時通訊軟體為本研究之個案。而經過一連串的流程與驗證,本研究所提的兩種前置處理程序,的確可以讓關鍵圖產生更容易判讀的關鍵圖圖形,並且藉此圖形可以讓觀圖者描繪更豐富且清晰的劇情。

並列摘要


One drawback of KeyGraph is its readability. Providing qualified document is helpful for KeyGraph to translate the document an appropriate and easy read KeyGraph diagram. And further, to discover worthy chances for decision making. We proposed 2 preprocessing strategies for assuring appropriate quality of documents, especially for decision makers when they try to discover chances with KeyGraph. A case study of Instant Messenger is used to verify the validity and readability of the proposed strategies. The results shown that the proposed strategies are helpful for creating readability KeyGraph diagrams, and further to create clear scenarios for discovering chances.

參考文獻


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