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題名：利用分類技術分析產品項目最適性之行銷組合
書刊名：電子商務學報
作者：陳垂呈
作者(外文)：Chen, Chui-Cheng
出版日期：2007
卷期：9:2
頁次：頁267-289
主題關鍵詞：資料探勘；分類分析；交易資料；行銷組合；Data mining；Classification analysis；Transaction data；Marketing mix
原始連結：連回原系統網址new window
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  • 共同引用共同引用:9
  • 點閱點閱:57
在本篇論文中，我們以消智者之交易資料為探勘的資料來源，每一筆交易資料包含有消費者曾經購買過的產品項目與其數量，以某些產品項目為探勘的目標，並視其他的產品為分類的屬性項目，分別從以下兩方面來發掘這些產品項目最適性的行銷組合：首先，我們只考量產品項目是否出現在交易資料中，在探勘的過程中，若交易資料包含有這些產品項目的比例值達到最小關聯度，則設定交易資料與這些產品項目之間的關聯性為「高」，否則設定為「低」。我們將交易資料進行分類分析以建構出一決策樹，從所建構出的決策樹中，可以找出那些屬性項目與這些產品項目之間的關聯性高，藉此做為發掘這些產品項目最適性之行銷組合的依據。再者，我們增加考量產品項目的購買數量，在探勘的過程中，若交易資料包含有這些產品項目的比例值達到最小數量關聯度，則設定交易資料與這些產品項目之間的關聯性為「高」，否則設定為「低」。我們提出一個方法來將包含有項目數量之交易資料進行分類分析以建構出一決策樹，從所建構出的決策樹中，可以找出那些屬性項目與這些產品項目之間的關聯性高，藉此做為發掘包含有項目數量之這些產品項目最適性的行銷組合的依據。此探勘結果，對企業在擬訂產品之行銷組合的策略時，將可以提供非常有用的參考資訊。
In this paper, we use consumers transaction data as the source data of mining. Each transaction data contains a consumer ever bought product items with quantity. We let some product items as the target of mining, and regard other products as attribute items for classification. We discover the most adaptive marketing mix of the product items from two aspects. First, we only consider product items whether they are contained in transaction data or not. In the mining process, we compute the percentage of transaction data contains the product items, and assign the association degree between the transaction data and the product items to be "high" if the percentage satisfies the minimum association threshold. Otherwise, it is "low". We classify the transaction data to construct a decision tree, and find out the at­tribute items that have the association degree to be high with the product items according to the decision tree. It is the basis to discover the most adaptive marketing mix of the product items. Moreover, we extra consider product items with quantity in the transaction data. In the mining process, we compute the percentage of transaction data contains the product items, and assign the association degree between the transaction data and the product items to be "high" if the percentage satisfies the minimum quantitative association threshold. Otherwise, it is "low". We propose a method to classify the transaction data with quantitative items for constructing a decision tree, and find out the attribute items that have the association degree to be high with the product items according to the decision tree. It is the basis to discover the most adaptive marketing mix with quantitative items of the product items. The results of the mining can provide very useful information to plan the strategy of marketing mix of products for the business.
期刊論文
1.陳彥良、許秉瑜、凌俊青(20010100)。在包裹資料庫中挖掘數量關聯規則。資訊管理學報，7(2)，215-229。new window  延伸查詢new window
2.Katharina, D. C. Stark、Dirk, U. Pfeiffer(1999)。The Application of Non-Parametric Techniques to Solve Classification Problems in Complex Data Sets in Veterinary Epidemiology--an Example。Intelligent Data Analysis，3(1)，23-35。  new window
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4.Han, J.、Pei, J.、Yin, Y.、Mao, R.(2004)。Mining frequent patterns without candidate generation: a frequent pattern tree approach。Data Mining and Knowledge Discovery，8(1)，53-87。  new window
5.Mak, B.、Munakata, T.(2002)。Rule Extraction from Expert Heuristics: A Comparative Study of Rough Sets with Neural Networks and ID3。European Journal of Operational Research，136(1)，212-229。  new window
6.Park, Jong-Soo、Chen, Ming-Syan、Yu, Philips S.(1997)。Using a hash-based method with transaction trimming for mining association rules。IEEE Transactions on Knowledge and Data Engineering，9(5)，813-825。  new window
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11.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
12.Quinlan, J. R.(1986)。Induction of Decision Trees。Machine Learning，1(1)，81-106。  new window
13.Coenen, F.、Leng, P.、Ahmed, S.(2004)。Data Structure for Association Rule Mining Trees and P-trees。IEEE Transactions on Knowledge and Data Engineering，16(6)，774-778。  new window
14.Tsai, P. S. M.、Chen, C. M.(2001)。Mining Quantitative Association Rule in a Large Database of Sales Transactions。Journal of Information Science and Engineering，7(4)，667-681。  new window
會議論文
1.Xu, B.(2005)。Managing Customer Satisfaction in Maintenance of Software Product Family via ID3。International Conference on Machine Learning and Cybernetics。Guangzhou, China。1820-1824。  new window
2.Agrawal, R.、Imielinski, T.、Swami, A.(1993)。Mining Association Rules between Sets of Items in Very Large Database。The ACM SIGMOD Conference on Management of Data，207-216。  new window
3.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
4.Ruckert, U.、Richter, L.、Kramer, S.(2004)。Quantitative Association Rules Based on Half-spaces: An Optimization Approach。0。507-510。  new window
5.Srikant, R.、Agrawal, R.(1996)。Mining Quantitative Association Rules in Large Relational Table。0。1-12。  new window
6.Konda, R.、Rajurkar, K. P.(2005)。A Rule Induction Algorithm for Continuous Data Using Analysis of Variance。0。489-494。  new window
7.Lee, C. F.、Shen, T. H.(2005)。An FP-split Method for Fast Association Rules Mining。0。459-463。  new window
8.Jearanaitanakij, K.(2005)。Classifying Continuous Data Set by ID3 Algorithm。0。1048-1051。  new window
9.Kang, W. H.、Kim, D. H.、Lee, S. W.(2005)。mBAR: A Materialized Bitmap Based Association Rule Algorithm。0。  new window
10.Ke, Y.、Cheng, I.、Ng, W.(2005)。MIC Framework: An Information-theoretic Approach to Quantitative Association Rule Mining。0。  new window
11.Pi, D. C.、Qin, X. L.、Gu, W. F.、Cheng, R.(2005)。STBAR: A More Efficient Algorithm for Association Rule Mining。0。1529-1533。  new window
學位論文
1.高淑珍(2004)。應用資料探勘於顧客回應模式之研究--以國內A壽險公司為例(博士論文)。國立成功大學。new window  延伸查詢new window
圖書
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3.魏志平、董和昇(2002)。電子商務理論與實務。華泰書局。  延伸查詢new window
4.Breiman, L.、Friedman, J. H.、Olshen, R. A.、Stone, C. J.(1984)。Classification and Regression Trees。Chapman & Hall/CRC。  new window
5.Han, Jiawei、Kamber, Micheline(2006)。Data Mining: Concepts and Techniques。San Francisco：Morgan Kaufmann Publishers。  new window
6.Berry, Michael J. A.、Linoff, Gordon S.(2004)。Data Mining Techniques for Marketing, Sales and Customer Relationship Management。Wiley Publishers。  new window
7.Quinlan, J. Rose(1993)。C4.5: Programs for Machine Learning。Morgan Kaufmann Publishers。  new window
 
 
 
 
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