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

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研究生：	蘭密雅 Mira - Aulia Dahlan
論文名稱：	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 Developing a Data Mining Approach to Investigate Association between demanders – A Study on PT Perkebunan Nusantara (PTPN) Indonesia
指導教授：	歐陽超 Chao Ou-Yang
口試委員:	郭人介 Ren-Jieh Kuo 楊朝龍 Chao-Lung Yang
學位類別：	碩士 Master
系所名稱：	管理學院 - 工業管理系 Department of Industrial Management
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	76
中文關鍵詞：	資料探勘、關聯分析、apriori演算法
外文關鍵詞：	Data mining, association rule, apriori
相關次數：	點閱：263 下載：6
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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。

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.

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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