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研究生:黃正明
研究生(外文):Cheng-Ming Huang
論文名稱:物件導向資料探勘及其應用
論文名稱(外文):Object-Oriented Data Mining and Its Applications
指導教授:洪西進洪西進引用關係洪宗貝洪宗貝引用關係
指導教授(外文):Shi-Jinn HorngTzung-Pei Hong
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:160
中文關鍵詞: 資料探勘 行動探勘 模糊理論 物件導向  網站探勘
外文關鍵詞:object-orientedweb miningdata miningmobility miningfuzzy sets
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資料探勘是從現有的數據庫中選出合乎需要的知識、或者值得瞭解的樣式之過程。 近年來,物件概念在多種應用方面深受歡迎且廣被使用,尤其適用於複雜的資料描述。 這篇論文針對如何從二元的物件導向交易資料、及網站伺服器的記錄中找出相關知識提出兩種新演算法, 也同時利用數值化物件導向的交易資料、及上述網站伺服器的記錄中提出兩種模糊資料探勘演算法。
在應用上,這篇論文嘗試在無線網路中發現模糊個人行動樣式以幫助系統提供個人化服務。其將每個行動用戶在各地方區域的到達時間及所停留的時間當成重要的屬性來表示所得結果,由於到達時間及停留期間為數值型態,所以利用模糊概念來處理它們以形成語意項目。此外,本論文亦提出一個基於AprioriAll演算法的模糊探勘演算法,但是有許多針對行動網路的特殊考量和AprioriAll演算法不盡相同,這些差異造成在設計演算法時需有更細緻的考量。
Data mining is a process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the object concept has been very popular and used in a variety of applications, especially for complex data description. This thesis thus proposes two novel data-mining algorithms for extracting interesting knowledge from transactions stored as object data and logs of object-oriented web server. We also proposed two novel fuzzy data-mining algorithms for extracting interesting knowledge from quantitative transactions which are stored as object data and log of quantitative object-oriented web server.
In applications, this thesis attempts to discover fuzzy personal mobility patterns for assisting systems in providing personalized service in a wireless network. The arrival time and the duration time of each location area visited by a mobile user are used as important attributes in representing the results. Since both the arrival time and the duration time are numeric, fuzzy concepts are used to process them and to form linguistic terms. A fuzzy mining algorithm has then been proposed, which is based on AprioriAll algorithm, though different in several ways aspects. The difference arises more delicate considerations in the design of the proposed algorithm.
摘 要 II
ABSTRACT III
List of Figures VII
List of Tables X
CHAPTER 1 Introduction 1
1.1 MOTIVATION 1
1.2 CONTRIBUTIONS 4
1.3 READER'S GUIDE 4
CHAPTER 2 Review of Related Concepts 5
2.1 OBJECT-ORIENTED TRANSACTIONS 5
2.2 ASSOCIATION RULES 6
2.3 SEQUENTIAL PATTERNS 7
2.4 WEB MINING 8
2.5 FUZZY SET CONCEPTS 9
2.6 FUZZY MINING 12
2.7 WIRELESS NETWORKS FOR MOBILE USERS 12
CHAPTER 3 Mining Inter- and Intra- Object-Oriented Association Rules 14
3.1 NOTATIONS 14
3.2 THE PROPOSED ALGORITHM 15
3.3 AN EXAMPLE 17
CHAPTER 4 Mining Fuzzy Inter- and Intra- Object-Oriented Association Rules 28
4.1 NOTATIONS 28
4.2 THE PROPOSED ALGORITHM 29
4.3 AN EXAMPLE 33
CHAPTER 5 Object-Oriented Web Usage Mining 43
5.1 NOTATIONS 43
5.2 THE PROPOSED ALGORITHM 44
5.3 AN EXAMPLE 47
CHAPTER 6 Linguistic Object-Oriented Web Mining 57
6.1 NOTATION 57
6.2 THE PROPOSED ALGORITHM 58
6.3 AN EXAMPLE 63
CHAPTER 7 Mobility Knowledge Discovery in Wireless Networks 80
7.1 NOTATIONS 81
7.2 THE PROPOSED ALGORITHM 81
7.3 AN EXAMPLE 84
CHAPTER 8 Linguistic Mobility Patterns Mining 92
8.1 NOTATIONS 93
8.2 THE PROPOSED ALGORITHM 94
8.3 AN EXAMPLE 98
CHAPTER 9 Experimental Results 111
9.1 EXPERIMENTAL RESULTS FOR OBJECT-ORIENTED DATA MINING 111
9.2 EXPERIMENTAL RESULTS FOR FUZZY OBJECT-ORIENTED DATA MINING 116
9.3 EXPERIMENTAL RESULTS FOR OBJECT-ORIENTED WEB MINING 123
9.4 EXPERIMENTAL RESULTS FOR LINGUISTIC OBJECT-ORIENTED WEB MINING 128
9.5 EXPERIMENTAL RESULTS FOR MOBILITY MINING 134
9.6 EXPERIMENTAL RESULTS FOR LINGUISTIC MOBILITY MINING 137
CHAPTER 10 Conclusions and Future Works 141
References 144
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