以頻繁項探勘區域性購物行為

資料探勘(Data Mining)技術是由資料中挖掘出有用的特徵樣式程序，進而利用發掘出之樣式來解釋現存的行為或預測未來的結果，這些是一些傳統資料分析工具所無法解決的問題，本文之目標在於應用資料探勘技術從業界交易資料庫中，挖掘感興趣的關聯規則(Association Rule)，進而發現銷售商品彼此之間的關聯度以及商品和目標間的關係。網際網路的興起對目前的生活型態及商業模式產生關鍵性的影響，而電子商務的交易方式和一般商店的消費方式有什麼不同，關健在於區域，傳統的行銷方式行銷會以商家附近開始進行行銷，但電子商務卻大大的不同，它打破了傳統的購物方式，因為在電子平台的世界中已經沒有所謂的區域，若可分析出各個區域的購物行為，便可得知該區域所欠缺商品，進而可針對不同的區域進行不同類別商品的行銷，並可從分析資料中，輕易的得到潛藏的客戶群。本文首先會使用Apriori演算法，分析交易資料庫中的資訊，但也由於Apriori 演算法有相當大的瓶頸，所以後續所提出的頻繁型樣樹法(Frequent Pattern Tree) 與頻繁型樣串列法(Frequent Pattern List)皆是屬於可彌捕Apriori的方法，在本文中將深入探討Apriori與FP-tree以及FP-list的建構方式，並以電子商務業界中實際交易的資料庫進行分析比較，可知FP-tree分析方式所帶來的優點。

關鍵字

資料探勘；關聯規則；頻繁型樣樹法；頻繁型樣串列法

並列摘要

There is no doubt that the rapid growth of e-commerce has significantly impacted on consumers’ purchase behaviors and an organization’s marketing strategies. One may ask what is the difference between e-commerce websites and traditional brick-and-mortar stores. A major difference is that a traditional store begins its business by attracting customers in the area or plaza where it is located, while an e-commerce website enables the marketers to access customers globally without boundaries. Therefore, by analyzing the consumers’ purchase behaviors collected from different regions, information about what products are in need and what marketing strategies to be applied can be yielded. Also, with such information about market segmentation, a marketer can easily target and reach the potential customers by sending right messages. Data Mining, a technology that scrutinizes and sorts databases for meaningful correlations or patterns, is used to explain a current phenomenon or to provide models to predict future results. This method is incomparable because it provides information that traditional analyzing tools are not able to generate. The objective of this study is to apply the Association Rule to the marketing database and find out the correlations among the products as well as the relationships between the products and the target segments. Initially, this study attempts to use Apriori algorithm to analyze the data in the marketing database. However, due to some deficiency of Apriori, Frequent pattern tree and Frequent pattern list, the latest and most efficient algorithms, are later adopted. In this study, the constructions of Apriori, Frequent pattern tree, and Frequent pattern list are thoroughly discussed. Moreover, the author applies these methods to the marketing database. By analyzing and comparing them, this study suggests that the Frequent pattern related methods in Data Mining will be advantageous and profitable tools for all marketers of e-commerce.

並列關鍵字

Data Maining ； Apriori ； FP-Tree ； FP-List

參考文獻

[1]. Richard J. Roger, Michael W. Geatz, Data Mining: A tutorial-based primer, First edition, Boston, Addison Wesley, Chinese Version, 2003, pp.79-85

[3]. J. Han, J. Pei, Y. Yin, and R. Mao, “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach”, Journal of Data Mining and Knowledge Discovery, 2004, pp.53-87

[4].Fan-Chen Tseng, Ching-Chi Hsu, and Henry Chen, “Mining Frequent Closed Itemsets with the Frequent Pattern List.”, Proc. The 2001 IEEE International Conference on Data Mining, San Jose, California, USA, Nov.29–Dec.2, 2001.

[5]. Fan-Chen Tseng and Ching-Chi Hsu, “Creating Frequent Patterns with the Frequent Pattern List”, Proc. of Asia Pacific Conference of Data Mining and Knowledge Discovery, Hong Kong, 2001, pp.376-386

[6]. Xuequn S., K.U. Sattler, and I. Geist, “SQL Based Frequent Pattern Mining withFP-growth”, Lecture Notes in Computer Science, Springer Berlin/Heidelberg, Volume 3392/2005, Apr. 2005, pp.32-46

國際替代計量

以頻繁項探勘區域性購物行為

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