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題名:從購買意願資料中挖掘高度相關性的關聯規則
書刊名:資訊管理學報
作者:翁政雄
作者(外文):Weng, Cheng-hsiung
出版日期:2011
卷期:18:4
頁次:頁119-138
主題關鍵詞:資料探勘關聯規則關聯分析Data miningAssociation ruleCorelation analysis手機購買意願購買決策
原始連結:連回原系統網址new window
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  • 被引用次數被引用次數:期刊(3) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:3
  • 共同引用共同引用:0
  • 點閱點閱:45
關聯規則探勘技術是一項重要的資料挖掘技術,這項技術可以從交易資料庫中挖掘 消費者購買行為之間的關聯性。現今的行銷策略皆視顧客為公司重要的獲利來源。因 此,公司應該積極尋找潛在的顧客,並發展合適的行銷策略以吸引他們。為達上述目 的,許多公司已經開始積極收集相關資料庫,並嘗試從這些資料庫中找出有意義的規 則,藉以發展合適的行銷策略以吸引這些潛在顧客。本研究探討如何利用關聯規則分析 消費者購買手機的決策考量因素,利用關聯規則之支持度與信心度分析消費者基本資料 與手機產品特性之間的關聯性,以提供給行銷部門及產品設計部門分別作為行銷策略制 定之參考與設計出更符合消費者的產品。然而,使用-count方式累計過多具有低支持度 的項目集時,卻容易產生不具關聯性的高頻項目集。因此,本研究發展新的方法嘗試從 消費者的購買意願中挖掘有意義且有關聯性的規則。此方法乃是運用-cut的概念過濾不 具關聯性的低支持度項目,並且利用相關係數(lift)進一步強化現有挖掘關聯規則的基 本機制(支持度-信心度),嘗試從消費者購買意願資料中找出有意義且相關的規則。實 驗結果顯示本研究所提出的方法可以找出有價值且具有高度相關的關聯規則。
Association rule mining is an important data analysis method that can discover associations within data. This technique can mine the associations between the consumer’s behaviors. The current marketing strategies perceive customers as important resources to a company for making more profit. Therefore, it is essential to companies to successfully discover potential customers and then develop new marketing strategies to attract them. To achieve these aims, many companies have gathered significant numbers of large databases to discover meaningful patterns and then develop new marketing strategies to attract the potential customers. However, using the -count, the summation of a large number of itemsets with very small support may induce irrelevant associations. To this end, this study proposes a new approach to discover interesting and relevant patterns from consumer’s purchasing intension. This approach is based on the -cut method to filter out the irrelevant patterns with small support. Furthermore, a correlation measure, also known as lift, is used to augment the support-confidence framework for association rules. Next, we develop an algorithm to discover relevant and interesting association rules from purchasing intensions. Experimental results from the survey data show that the proposed approach can help to discover interesting and valuable patterns with high correlation.
期刊論文
1.Yang, Tzyy-Ching、Lai, Hsiangchu(2006)。Comparison of product bundling strategies on different online shopping behaviors。Electronic Commerce Research and Applications,5(4),295-304。  new window
2.Hong, T. P.、Kuo, C. S.、Chi, S. C.(1999)。Mining Association Rules from Quantitative Data。Intelligent Data Analysis,3(5),363-376。  new window
3.Lian, W.、Cheung, D. W.、Yiu, S. M.(2005)。An efficient algorithm for finding dense regions for mining quantitative association rules。Computers and Mathematics with Applications,50(3/4),471-490。  new window
4.Chen, Ming-Syan、Han, Jiawei、Yu, Philip S.(1996)。Data Mining: An Overview from a Database Perspective。IEEE Transactions on Knowledge and Data Engineering,8(6),866-883。  new window
5.Zadeh, L.A.(1971)。Quantitative fuzzy semantics。Information Sciences,3(2),159-176。  new window
6.Tseng, M.C.、Lin, W.Y.、Jeng, R.(2008)。incremental maintenance of generalized association rules under taxonomy evolution。Journal oflnformation Science,34(2),174-195。  new window
7.Chen, Y.L.、Tang, K.、Shen, R.J.、Hu, Y.H.(2005)。Market basket analysis in a multiple store, environment。Decision Support Systems,40(2),339-354。  new window
8.Bodjanova, S.(2002)。A generalized α-cut。Fuzzy Sets and Systems,126(2),157-176。  new window
9.Chen, Y.L.、Weng, C.H.(2008)。Mining association rules from imprecise ordinal data。Fuzzy sets and systems,159(4),460-474。  new window
10.Lin, C.、Hong, C.(2008)。Using customer knowledge in designing electronic catalog。Expert Systems with Applications,34(1),119-127。  new window
11.Hu, Y.C.、Chen, R.S.、Tzeng, G.H.(2003)。Discovering fuzzy association rules using fuzzy partition methods。Knowledge-Based System,16(3),137-147。  new window
會議論文
1.Agrawal, R.、Imielinski, T.、Swami, A. N.(1993)。Mining Association Rules between Sets of Items in Large Databases。The 1993 ACM SIGMOD International Conference on Management of Data,207-216。  new window
2.Agrawal, R.、Srikant, R.(1994)。Fast algorithms for mining association rules in large database。The 20th International Conference on Very Large Data Bases。Morgan Kaufmann Publishers Inc.。478-499。  new window
圖書
1.Kotier, Philip(2001)。A Framework for Marketing Management。Prentice-Hall。  new window
2.Han, Jiawei、Kamber, Micheline(2000)。Data mining: Concepts and techniques。Morgan Kaufmann Publishers。  new window
3.Berry, Michael J. A.、Linoff, Gordon S.(1997)。Data Mining Techniques for Marketing, Sales and Customer Support。John Wiley & Sons, Inc.。  new window
4.Shepard, D.(1998)。The New Direct Marketing: How to Implement a Profit-Driven Database Marketing Strategy。  new window
5.Djouadi, Y.、Redaoui, S.、Amroun, K.(2007)。Mining fuzzy association rules from uncertain data。IEEE International Fuzzy Systems Conference。London。  new window
6.Srikant, R.、Vu, Q.、Agrawal, R.(1996)。Mining Association Rules with Item Constraints。SIGMOD International Conference on Management of Data。  new window
其他
1.International Business Machines(1996)。IBM Intelligent Miner User's Guide。  new window
 
 
 
 
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