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研究生： 張礽川
Chang, Jeng-Chuan
論文名稱： 具時間特性之網頁瀏覽行為探勘與預測機制
Mining and Predicting User Navigation Patterns based on Web Temporality
指導教授： 曾新穆
Tseng, Shin-Mu
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2004
畢業學年度： 92
語文別： 中文
論文頁數： 60
中文關鍵詞： 時間性 、規則變化 、瀏覽樣式 、資料探勘 、網頁探勘
外文關鍵詞： Temporality, Rule Changes, Navigation Patterns, Web Mining
相關次數： 點閱：132  下載：8 
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    • 　　由於網際網路的普及與電子商務的發展，預測使用者在下一步可能瀏覽的網頁亦隨之受到相當的重視。在過去預測使用者行為的研究中，多是探勘使用者瀏覽路徑或是對相似網頁作叢集以建立使用者行為模型。然而，在這些研究中，卻沒有考慮時間性的因素，即使用者進入網站的時間。因此，本論文創新性地考慮了時間性的因素以探勘使用者行為瀏覽模型，並藉以預測使用者未來之瀏覽路徑。此外，我們為了了解時間性的變化，提出三個方法來評量，也利用時間性的變化來評估某個時間性因素是否會提升整體網站的預測準確率。最後，由我們的實驗可以驗證當時間性變化愈明顯或是愈不穩定，以該時間性瀏覽模型來預測使用者行為，則預測之準確率會有一定的提升。

    　　With the rapid growth of the World Wide Web and the development of E-commerce, mining and predicting user’s web browsing patterns have become a hot topic. The past researches on this field focus on mining users’ navigation patterns or clustering pageviews so as to model users’ behavior. However, none of them are concerned with the web log temporality, i.e., the start time of a user session in our definition. In this paper, we take into　account the Web temporality for constructing the time-based user behavior model, based on which the user behavior can be predicted. In addition, we propose three methods to measure the changes of Web temporality in order to evaluate the applicability of a temporality model. Our experiments show that the precision of prediction can be improved more if there exist more distinct changes of temporality in the user’s browsing behaviors.

    英文摘要......................................I 中文摘要.....................................II 誌謝........................................III 目錄.........................................IV 表目錄......................................VII 圖目錄.....................................VIII 第一章 導論...............................1 1.1 研究背景..............................1 1.2 研究動機..............................1 1.3 環境與問題描述........................2 1.4 研究方法..............................5 1.5 研究貢獻..............................7 1.6 論文架構..............................7 第二章 文獻探討...........................8 2.1 網路探勘之環境究......................8 2.1.1 資料來源..............................8 2.1.2 環境上的問題..........................9 2.1.3 分析應用.............................11 2.2 網路探勘預測機制.....................12 2.2.1 預測機制.............................13 2.2.2 關聯規則.............................13 2.2.3 預測規則整理.........................14 2.2.4 N-gram模型...........................15 2.2.5 馬可夫模型...........................15 2.2.6 規則挑選.............................19 2.3 應用時間性的網路探勘.................21 2.3.1 時間序關聯規則.......................21 2.3.2 時間性人次叢集.......................22 2.3.3 時間馬可夫模型.......................23 2.3.4 時間事件預測.........................23 2.4 本章總結.............................24 第三章 時間性瀏覽路徑模型與規則變化探勘..25 3.1 時間性N-gram模型.....................25 3.1.1 資料前處理...........................26 3.1.2 時間性N-gram模型.....................28 3.1.3 預測方法.............................28 3.2 時間性規則變化.......................32 3.2.1 卡方齊一性檢定.......................33 3.2.2 基本變化.............................34 3.2.3 規則支持度的基本變化.................35 3.2.4 規則信賴度的基本變化.................36 3.2.5 規則的預測變化.......................37 3.3 本章總結.............................39 第四章 實驗分析..........................40 4.1 實際資料之實驗分析...................41 4.1.1 實驗資料來源.........................41 4.1.2 實際資料之實驗結果與分析.............42 4.2 模擬資料之實驗分析...................48 4.3 實驗總結.............................54 第五章 結論與未來研究方向................55 5.1 結論.................................55 5.2 應用與未來研究方向...................55 5.2.1 自動化時間性模型.....................55 5.2.2 時間性網頁之預測.....................56 參考文獻.....................................57

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