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研究生:陳庭鈞
研究生(外文):Chen,Tin Jiun
論文名稱:選擇商業應用資料探勘方法之框架
論文名稱(外文):A Framework for Selecting Data Mining Method in Business Application
指導教授:諶家蘭諶家蘭引用關係季延平季延平引用關係
指導教授(外文):Seng,Jia LangChi,Yen Ping
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
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:72
中文關鍵詞:資料探勘商業應用選擇方法資料探勘演算法
外文關鍵詞:Data miningBusiness applicationSelection methodData mining algorithm
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由於資訊科技的進步與網路的普及,企業得以收集與儲存大量的資料。使用資訊工具來協助資料處理、資訊擷取、以及產生知識已然變成企業的重要課題之一,所以如何良好運用資料探勘工具成為使用者關注的焦點。由於並非每一個使用者對於資料探勘的原理都有充分的了解,所以如何從探勘工具提供的功能中選用最佳的解決方案並不容易。如果對於探勘結果不滿意而需要調整軟體邏輯,與IT人員的協商溝通卻又曠日費時。

為了解決這個問題,本研究提出一個演算法選擇方法,藉由分析商業應用的內容,來自動對應到特定的資料探勘方法與演算法,讓選擇演算法的過程更為快速、更系統化,提升利用資料探勘工具解決商業問題的效率。
Due to the information technology improvement and the growth of internet, companies are able to collect and to store huge amount of data. Using data mining technology to aid the data processing, information retrieval and knowledge generation process has become one of the critical missions to enterprise, so how to use data mining tools properly is users’ concern. Since not every user completely understand the theory of data mining, choosing the best solution from the functions which data mining tools provides is not easy. If user is not satisfied with the outcome of mining, communication with IT employees to adjust the software costs lots of time.

To solve this problem, a selection model of data mining algorithms is proposed. By analyzing the content of business application, user’s requirement will map to certain data mining category and algorithm. This method makes algorithm selection faster and reasonable to improve the efficiency of applying data mining tools to solve business problems.
Table of Contents I
List of Tables III
List of Figures IV
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Objectives 1
1.3 Research Issues 2
1.4 Research Limitation 3
1.5 Research Flow 3
1.6 Organization of the Thesis 4
Chapter 2 Literature Review 5
2.1 Data Mining 5
2.1.1 Data Mining Definition 6
2.1.2 Data Mining Structure 8
2.1.3 Data Mining Method 9
2.1.4 Data Mining Modeling 11
2.2 Commercial Applications 12
2.3 Commercial Applications Related Works 14
2.4 Summary 23
Chapter 3 Research Method 24
3.1 Business Applications Analysis 24
3.2 Data Mining Algorithms Analysis 29
3.2.1 Association Rule Algorithms Analysis 32
3.2.2 Classification Rule Algorithms Analysis 33
3.2.3 Prediction Algorithms Analysis 34
3.2.4 Clustering Algorithms Analysis 34
3.3 Mapping Business Characteristic to Mining Concept 35
3.4 A Selection Model in the Application of Data Mining 37
3.4.1 Research Structure and an Example 37
3.4.2 Business Side 40
3.4.3 Mining Side 41
3.4.4 Selection Model 42
3.5 Summary 42
Chapter 4 Prototype Implementation 44
4.1 Prototype Platform and System Structure 44
4.2 Prototype System Design 44
4.2.1 Database Design 44
4.2.2 Function Design 46
4.3 Prototype System Implementation 46
Chapter 5 Research Experiment 52
5.1 Experimental Design 52
5.2 Test Cases 52
Chapter 6 Research Discussion 58
6.1 Managerial Findings 58
6.2 Technical Findings 59
Chapter 7 Conclusions and Future Research Directions 61
7.1 Conclusions 61
7.2 Future Research Directions 61
References 63
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