博碩士論文 etd-0713112-165207 詳細資訊


姓名 蘭密雅 (Mira Aulia Dahlan) 電子信箱 mira.a.dahlan@gmail.com
學號 M10001828 論文著作權 作者與指導教授共同擁有
系所名稱(中) 工業管理系 系所名稱(英) Department of Industrial Management
學年度 / 學期 100學年度第2學期 學位 碩士 (Master)
論文名稱(中) 資料探勘技術於需求者關聯性之探討 -以印尼PT Perkebunan Nusantara (PTPN)公司為例
論文名稱(英) Developing a Data Mining Approach to Investigate Association between demanders – A Study on PT Perkebunan Nusantara (PTPN) Indonesia
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論文使用權限 校內立即公開、校外立即公開
論文種類 碩士論文
論文語文別 / 頁數 英文 / 76
統計 已被瀏覽 1114 次,被下載 139 次
關鍵字(中)
  • apriori演算法
  • 關聯分析
  • 資料探勘
  • 關鍵字(英)
  • apriori
  • association rule
  • Data mining
  • 摘要(中) PT Perkebunan Nusantara (PTPN) 是一家生產農產品的國營企業,為了銷售產品,PTPN透過子公司KPBN進行公開招標。為了定義需求者行為和探討需求者關聯,本研究使用apriori 演算法進行需求者關聯的探勘。以建議公司找出具有潛力的需求者。透過需求者行為也可以協助公司找到頻繁投標者。整個研究過程是經由七個月的資料收集,並將資料分成三群,接著進行關聯分析並計算關聯程度以驗證和證實規則。
    摘要(英) PT Perkebunan Nusantara (PTPN) is a state-owned company which produces agricultural product. To sell its product, PTPN has subsidiary named KPBN which held an open bidding. In order to identify demander behavior and to investigate demander association, association between demanders can be identified using the apriori algorithm. It could be as a suggestion to that supplier to find the most potential demander. The behavior of demanders also may help the supplier to find out who the frequent bidder is. The process of this research is started with pre-processing the data that had already been collected for seven months, spliting those data into three groups, generating the association analysis, and then calculating the degree association to verify and validated the rule.
    論文目次 Table of Contents
    中文摘要 ii
    Abstract iii
    Acknowledgements iv
    List of Figures viii
    List of Tables ix
    Chapter 1 1
    1. Introduction 1
    1.1 Background 1
    1.2 Motivation 2
    1.3 Objective 3
    1.4 Scope and Constraint 4
    1.5 Organization of Thesis 4
    Chapter 2 6
    2. Literature Review 6
    2.1 Data Mining 6
    2.2 Association Analysis 8
    2.3 Alternative Objective of Interesting Measure 12
    Chapter 3 15
    3. Research Methodology 15
    3.1 Design Phase 15
    3.2 Data pre-processing 16
    3.3 Split the data 17
    3.4 Generating association analysis 17
    3.5 Calculating degree of association between demanders 20
    Chapter 4 22
    4. Model Implementation 22
    4.1 Experimental scenario 22
    4.2 Data pre-processing 24
    4.3 Data Split 26
    4.4 Discovering demander association 27
    4.4.1 Generating frequent itemsets and candidate pruning 27
    4.4.2 Generate association rule 29
    4.5 Measuring degree of association 30
    4.1 Result Analysis and Discussion 37
    4.1.1 Result Analysis 37
    4.1.2 Discussion 41
    Chapter 5 43
    5. Conclusion and Future Research 43
    5.1 Conclusion 43
    5.2 Future Research 44
    Reference 45
    Appendices 47
    Appendix A. Raw data 47
    Appendix B. Data Split with its number 48
    Appendix C-1. PTPN Frequent Itemsets 49
    Appendix C-2. Province Frequent Itemsets 53
    Appendix C-3. Island Frequent Itemsets 56
    Appendix D-1. PTPN Association Rule 57
    Appendix D-2. Province Association Rule 59
    Appendix D-3. Island Association Rule 61
    Appendix E-1. Contingency table for PTPN Branch 62
    Appendix E-2. Contingency table for Province 66
    Appendix E-3. Contingency table for Island 69
    Appendix F-1. Available rules for province 70
    Appendix F-2. Available rules for island 70
    Appendix G-1. Calculation result of association between demanders-Province 71
    Appendix G-2. Calculation result of association between demanders-Island 72
    Appendix H-1. Ranking result of association between demanders – Province 73
    Appendix H-2. Ranking result of association between demanders-Island 74
    Appendix I-1. Ranking of demander pair association - Province 75
    Appendix I-2. Ranking of demander pair association -Island 76
    Appendix J. Map of Indonesia 77
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    [20] www.kpbptpn.co.id
    指導教授/口試委員
  • 歐陽超 - 指導教授
  • 郭人介 - 委員
  • 楊朝龍 - 委員
  • 繳交日期 2012-07-17


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