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| Title: | 資料採礦實務應用 - 以關聯規則分析E-ICP商品消費資料 |
| Authors: | 何玉芝 |
| Contributors: | 鄭宇庭
何玉芝 |
| Keywords: | 資料採礦
關聯規則
購物籃分析
行銷 |
| Date: | 2002 |
| Issue Date: | 2009-09-14 |
| Abstract: | 20世紀電腦、網路產業的快速發展，使得人類能夠藉由更快速的電腦處理更大量的資料，但也因此產生了更多的問題。資料採礦技術發展的目的便在於解決分析大量資料時所遇到的問題。
商業領域的資料採礦發展十分迅速，因為由資料採礦得到的資訊與知識能夠幫助行銷決策者訂定最佳的行銷策略。終極目標是將公司內部、外部的資訊串連起來，經由工作循環以得到商業智慧。
目前台灣許多行銷領域的研究單位都在進行消費者資料的蒐集。本研究以E-ICP資料庫為研究目標，利用資料採礦方法挖掘尚未被研究者發現的知識。由於過去E-ICP資料的運用較少觸及商品消費的整體探討，但商品消費概況卻佔E-ICP資料相當大的比重，因此試以關聯規則分析為工具，進一步瞭解商品間的關係。
藉由本次實證研究的經驗發現關聯規則分析在實務上不適用之處，這樣的回饋對於未來研究關聯規則分析的研究者而言，能夠提供許多值得深究的方向。
The swift developments of Computer Science and Internet in 20 century enable people handling more and more data, but bring even more problems. Data Mining is then developed to solve them.
Data Mining is very popular in business environment, because all the information and knowledge gained can help managers make the best decisions. And in the long run, Data Mining can help the circulation of information inside and outside an organization.
In Taiwan, many research centers are collecting consuming data in order to understand more about consumer behaviors. This study is in focus of E-ICP data which has a long history in consumer issues. The commodities data in E-ICP dataset is very abundant, but less emphasis was made upon it. Therefore, using Association Rules to find out the relationship between commodities is a good trial.
The process of analyzing E-ICP data with Association Rules let us realize how difficult to take it into practice. And the problems I faced and the solutions I used in this study could feedback to future analyzer for some meaningful research issues. |
| Reference: | [1] Margaret H. Dunham (2003), “Data Mining Introductory and Advanced Topics”, Prentice Hall.
[2] Michael J. A. Berry, Gordon S. Linoff (1997), “Data Mining Techniques: for marketing, sales, and customer support”, John Wiley & Sons.
[3] Michael J. A. Berry, Gordon S. Linoff (2000), “Mastering Data Mining, The Art & Science of Customer Relationship Management”, John Wiley & Sons.
[4] Rakesh Agrawal, Tomasz Imielinski, Arun Swami (1993), “Mining Association Rules between Sets of Items in Large Databases”, ACM SIGKDD Conference on Management of Data, pages 207-216.
[5] Ramakrishnan Srikant, Quoc Vu, Rakesh Agrawal (1997), “Mining Association Rules with Item Constraints”, ACM SIGKDD Conference on Management of Data, pages 67-73.
[6] Ke Wang, Senqiang Zhou, Jack Man Shun Yeung, Qiang Yang (2003), “Mining Customer Value: From Association Rules to Direct Marketing”, The IEEE International Conference on Data Engineering.
[7] Chengqi Zhang, Shichao Zhang (2002), “Association Rule Mining: Models and Algorithms”, Springer.
[8] David J. Hand (1999), “Statistics and Data Mining: Intersecting Disciplines”, ACM SIGKDD Explorations, Vol. 1, Issue 1, pp.16-19.
[9] Bing Liu, Wynne Hsu, Yiming Ma (1999), “Mining association rules with multiple supports”, ACM SIGKDD Conference on Management of Data, pages 337-341.
[10] Alex Berson, Stephen Smith, Kurt Thearling (1999), “Building Data Mining Applications for CRM”, McGraw-Hill.
[11] 郭家佑(1999)，「如何在資料庫中發掘空間性週期關聯規則-以便利商店交易資料為例」，國立政治大學資訊管理學系碩士論文。
[12] 簡利曲(2001)，「運用Data Mining之購物籃分析探討網路購物之最適產品組合」，國立台北大學企業管理學系碩士論文。
[13] 蔡明憲(2002)，「以混合式資料探勘技術強化客戶保留之工作」，國立台灣科技大學電子工程系碩士論文。
[14] 謝佳蓉(2000)，「市場資料中最佳關聯法則之探勘」，長榮管理學院經營管理研究所碩士論文。
[15] 沈明賢(2001)，「差異性顯示對關聯法則使用之評估」，元智大學資訊管理學系碩士論文。
[16] 戴玉旻(2001)，「圖書館借閱記錄探勘系統」，國立交通大學資訊科學研究所碩士論文。
[17] 王錫中(2002)，「運用關聯法則技術於產品開發設計之研究」，元智大學工業工程與管理研究所碩士論文。
[18] 卓叔靜(2001)，「資料探勘中關聯法則信賴度的假設檢定」，國立台灣科技大學資訊管理系碩士論文。 |
| Description: | 碩士
國立政治大學
統計研究所
90354009
91 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0090354009 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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