臺灣博碩士論文加值系統

資料探勘是近年來一項熱門的研究方向，其主要目的是從一群資料中探勘出有用的規則；資料探勘技術應用於網際網路環境中則稱為網路探勘，其中之網路習慣探勘目的是針對使用者網頁瀏覽的行為做分析並歸納有用之規則。目前網路探勘的研究資料來源大多以網站之日誌檔(Log Files)為主，演算或程式技術的精進，往往仍無法有效提昇探勘結果於實務應用上的適用性，而實務經驗顯示演算技術層次翻新，仍不如資料來源的多元豐富化有效。一些Log Files紀錄之外的瀏覽行為，例如複製、拉動捲軸或另存目標等，對網站管理者或探勘者雖然沒有明確的動機或目的，但是隱含著許多有用的網路探勘訊息，本研究稱為「意圖行為」。意圖行為乃使用者內心之目的表現於外之瀏覽行為，故其成為具探勘價值之資料來源，其對現有網路探勘成效的影響，原則上有「互補」及「增強」，而文獻中對其應用上仍缺乏的一項先決功能為線上蒐集使用者之瀏覽行為。
鑒於意圖行為這類豐富多元之資料來源，可能帶來網路探勘研究與應用上之柳暗花明又一村之影響，本研究欲從事含意圖行為之網路瀏覽行為之基礎研究，並實際探討意圖行為對於網路習慣探勘可能之「互補」及「增強」成效。因此，於基礎研究部分，本研究先從意圖行為定義與瀏覽行為分類導入著手，並進一步提出一線上瀏覽資料蒐集之運作機制，以期為意圖行為之應用奠下基礎。於意圖行為可能帶來之網路習慣探勘成效之部分，本研究以網路交易探勘(Web Transaction Mining,WTM)與模糊網路探勘(Fuzzy, Web Mining Algorithm, FWMA)兩種網路探勘演算法為例說明與探討。以意圖行為為基礎之IWTM (Intentional-Based WTM)演算法，分為有購物行為(IWTMP)與無購物行為(IWTMNP)兩種介紹：以延續原WTM演算法，融入意圖行為之IWTMP閳示其說明「增強」WTM應用上之可能效益；並以IWTMNP 示範其如何「互補」WTM無法使力之無購物網頁商品部分。以意圖行為為基礎之IFWMA(Intentional-Based FWMA)演算法，主要在處理瀏覽時間過長所帶來資料不精確之問題，分為在演算法中套用意圖行為與資料前置處理兩部分敘述，以兩者如何加強網路探勘資料準確性之「增強」。最後本研究並已一虛擬之交易網站，模擬並蒐集瀏覽者行為，進行上述四種演算法之實驗，並以實驗結果討論在實務上之應用之可能性。

Data mining is a popular research area in recent years, whose goal is to mining useful rules from a large data set. It’s named web mining when data mining is applied over the Internet, and web usage mining, one of its stems, which aims at analyzing user browsing behavior for useful patterns or rules. Currently, the major source for web mining is the web log files, which can’t promote its usability in practical applications despite the progresses or innovations on the algorithmic or programming techniques. Experiences indicate that the innovations in techniques demonstrating far less effects than the variety of the data sources Browsing behaviors outside the log files, such as ‘copy’, ’using scrollbar ’ or ‘save as’, although show no obvious motives or purposes, many useful information is hidden for web mining. We call these “Intentional Behavior”, which reflects a web user’s internal intend on their external behavior and becomes a valuable mining source. Intentional behavior presents the effects of complementary and enhancement in web mining. However, the literature lacks the prerequisite of such applications, an online collection mechanism of browsing behaviors.
According to the potential benefits intentional behavior could have brought into web mining research and applications, the study conducts a basic research of browsing behavior, and probes into the potential effect of complementary and enhancement in web mining. Within the basic research, we begin by defining intentional behavior and classifying the browsing behavior, and bringing up an open framework for collecting online browsing behavior as the basis in related applications. Within probing the effects of the web usage mining, we illustrate by examples of WTM Algorithm (Web Transaction Mining) and FWMA (Fuzzy Web Mining Algorithm). The IWTM (Intentional-Based WTM) is further divided into Purchase (IWTMP) and No-Purchase (IWTMNP)：extending from WTM,. IWTMP integrates intentional behavior for explaining the potential benefits of enhancements while IWTMNP addresses what the WTM cant not handle via intentional behavior for its complementary. The intentional-based FWMA (IFWMA) is improving the accuracy issue caused by the unreasonable long browsing time within the algorithm of FWMA while data pre-processing can be used before applying to the algorithm itself. Both the IFWMA and the data pre-processing generate enhancements on the data accuracy. At the end, this research setup a virtual website, simulate and collect browsing behavior from users, and run the four algorithms with these simulated data sets, and discuss their application in commercial applications accordingly.

目錄
中文摘要………………………………………………………………………II
英文摘要………………………………………………………………………IV
誌謝………………………………………………………………………… VII
目錄…………………………………………………………………………VIII
第一章 緒論
1.1 研究背景…………………………………………………………………1
1.2 研究動機…………………………………………………………………1
1.3 研究目的…………………………………………………………………3
1.4 研究流程…………………………………………………………………3
1.5 研究重要性………………………………………………………………5
1.6 研究範圍與限制…………………………………………………………5
第二章 文獻探討
2.1 資料探勘…………………………………………………………………6
2.2 網路探勘…………………………………………………………………8
2.3 網路習慣探勘的技術……………………………………………………9
2.4 瀏覽行為紀錄方法………………………………………………………16
2.5 瀏覽行為應用……………………………………………………………17
2.6 網路行銷……………………………………………………………… 19
第三章 研究架構
3.1 研究問題…………………………………………………………………21
3.2 研究情境…………………………………………………………………21
3.3 研究架構…………………………………………………………………22
3.4 研究限制…………………………………………………………………25
第四章 意圖行為定義與分類
4.1 意圖行為定義……………………………………………………………27
4.2 瀏覽行為分類……………………………………………………………27
4.3 瀏覽行為分類表…………………………………………………………30
4.4 線上蒐集運作機制………………………………………………………32
4.5 問題討論與運作機制範例介紹…………………………………………35
第五章 整合意圖行為之網路探勘演算法
5.1 以「意圖行為」為基礎之WTM演算法………………………………… 38
5.1.1 定義與符號說明………………………………………………………38
5.1.2 整合意圖行為之WTM演算法………………………………………… 40
5.1.3 IWTM演算法……………………………………………………………41
5.1.4 範例介紹與問題討論…………………………………………………43
5.2 以「意圖行為」為基礎之模糊網路探勘演算法………………………50
5.2.1 模糊網路探勘演算法(FWMA)簡介……………………………………51
5.2.2 意圖行為於FWMA演算法內部之改良方式 IFWMA………………… 54
第六章 實驗模擬
6.1 實驗環境與模擬資料設計………………………………………………59
6.1.1 虛擬網站介紹…………………………………………………………59
6.1.2 實驗數據對象與方式……………………………………………60
6.1.3 部分原始數據…………………………………………………………61
6.2 實驗假設與實驗限制……………………………………………………63
6.2.1 實驗假設………………………………………………………………63
6.2.2 實驗範圍………………………………………………………………63
6.3 IWTM實驗結果與分析……………………………………………………64
6.3.1 IWTMP有購買商品…………………………………………………… 64
6.3.2 IWTMp應用上之意含………………………………………………… 66
6.3.3 IWTMNP無購買商品……………………………………………………68
6.3.4 IWTMNP應用上之意含…………………………………………………70
6.4 IWFMA演算法…………………………………………………………… 72
6.4.1 IFWMA改良之演算法………………………………………………… 72
6.4.2 原先之FWMA演算法……………………………………………………80
6.4.3 IFWMA演算法應用之意含…………………………………………… 83
6-5 資料前置處理……………………………………………………………84
6.5.1 實驗說明與實驗步驟…………………………………………………84
6.5.2 資料前置處理應用之意含……………………………………………92
第七章 討論與未來研究方向
7.1 綜合結論…………………………………………………………………94
7.2實務應用之綜合討論…………………………………………………… 96
7.3 研究貢獻…………………………………………………………………98
7.4 未來研究方向……………………………………………………………99
參考文獻…………………………………………………………………… 103
附錄一……………………………………………………………………… 105

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