Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/19442
標題: 應用Data Mining及RFID技術於購物路徑及行為分析之研究
Application of Data Mining and RFID Technologies to Shopping Paths and Behavior Analysis
作者: 林瑋智
LIN, WEI-CHIH
關鍵字: RFID;資料集產生器;DATASET GENERATOR;PREFERRED SHOPPING PATH;SHOPPING PATH ANALYSIS;SHOPPING BEHAVIOR ANALYSIS;購物路徑分析;購物行為分析;喜好瀏覽路徑
出版社: 資訊科學系所
引用: [1] Bin Wang, Zhijing Liu, “Web mining research,” Computational Intelligence and Multimedia Applications, 2003, ICCIMA 2003, Proceedings, Fifth International Conference on 27-30 Sept. 2003 Page(s):84–89. [2] C. H. Yun and M. S. Chen, “Using Pattern-Join and Purchase-Combination for MiningWeb Transaction Patterns in an Electronic Commerce Environment,”in Proceedings of IEEE COMPSAC,Oct. 2000, pp.99-104. [3] Dongshan Xing*, Junyi Shen, “Efficient data mining for web navigation Patterns,” Information and Software Technology, Volume: 46, Issue: 1, January 1, 2004, pp. 55-63. [4] E. Masciari, “RFID Data Management for Effective Objects Tracking,”in Proceedings of ACM SAC,Mar.11-15, 2007, pp.457-461. [5] G. Roussos, “Enabling RFID in Retail,”IEEE Compute Magzine, March 2006, pp. 25-30. [6] H. Gonzalez, J. Han, and X. Li, “Mining Compressed Commodity Workflows from Massive RFID Data Sets,”in Proceedings of ACM CKIM, Nov. 5-11, 2006, pp.162-171. [7] Jian-guo Liu, Zheng-hong Huang, Wei-ping Wu, “Web mining for electronic business application,” Parallel and Distributed Computing, Applications and Technologies, 2003, PDCAT''2003. Proceedings of the Fourth International Conference on 27-29 Aug. 2003 Page(s):872 – 876. [8] James Liu, Jane You, “Smart Shopper: An Agent-Based Web-Mining Approach to Internet Shopping,” Fuzzy Systems, IEEE Transactions on Volume 11, Issue 2, April 2003 Page(s):226 - 237 Digital Object Identifier 0.1109/TFUZZ.2003.809900. [9] Lizhen Liu, Junjie Chen, Hantao Song, “The research of Web mining,” Intelligent Control and Automation, 2002, Proceedings of the 4th World Congress on Volume 3, 10-14 June 2002 Page(s):2333 - 2337 vol.3 Digital Object Identifier 10.1109/WCICA.2002.1021507. [10] Ming-Syan Chen, Jong Soo Park, Jong Soo Park, “Efficient data mining for path traversal patterns,” Knowledge and Data Engineering, IEEE Transactions on Volume 10, Issue 2, March-April 1998 Page(s):209 - 221 Digital Object Identifier 10.1109/69.683753 [11] Pengyue J. Lin., Behrokh Samadi, Daniel R. Jeske, Sean Cox, Carlos Rendon, Douglas Holt, Rui Xiao, ” Development of a Synthetic Data Set Generator for Building and Testing Information Discovery Systems,” Information Technology: New Generations, 2006, ITNG 2006, Third International Conference on 10-12 April 2006 Page(s):707 - 712 Digital Object Identifier 10.1109/ITNG.2006.51 [12] Pang-Ning Tan, Michael Steinbach, Vipin Kumar,” Introduction to Data Mining,” Addison Wesley, 2005. [3] Rui Wu, Wansheng Tang, Ruiqing Zhao, “An Efficient Algorithm for Fuzzy Web-Mining,” Information Reuse and Integration, 2004, IRI 2004, Proceedings of the 2004 IEEE International Conference on 8-10 Nov. 2004 Page(s):576 - 581 Digital Object Identifier 10.1109/IRI.2004.1431523. [14] R. Cooley, B. Mobasher, and J. Srivastava, “Web mining: information and pattern discovery on the World Wide Web,” Tools with Artificial Intelligence, 1997, Proceedings, Ninth IEEE International Conference on 3-8 Nov. 1997 Page(s):558 - 567 Digital Object Identifier 10.1109/TAI.1997.632303. [15] Yue-Shi Lee, Show-Jane Yen, Min-Chi Hsieh, Ghi-Hua Tu, “Mining Web transaction patterns in electronic commerce environment,” E-Commerce Technology for Dynamic E-Business, 2004, IEEE International Conference on 2004 Page(s):74 - 81 Digital Object Identifier 10.1109/CEC-EAST.2004.46. [16]日經BP RFID技術編輯部編、周湘棋譯,RFID技術與應用,旗標出版股份有限公司,2004年。 [17]社團法人日本自動辨識系統協編、刁建成譯,RFID原理與應用,全華科技股份有限公司,2005年。 [18]荒川弘熙編、NTT Data Ubiquitous研究會著、葉珠娟等翻譯,實現「無遠弗屆」的RFID技術,向上出版事業有限公司,2005年。 [19]邱瑩青著、葉怡惠、胡珍綺等編輯,RFID實踐非接觸式智慧卡系統開發,學貫行銷股份有限公司,2005年。 [20]謝建新、游戰清、張義強、戴青雲編著,RFID理論與實務-無線射頻識別技術,網奕資訊科技股份有限公司,2006年。 [21]曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯著,資料探勘(Data Mining),旗標出版股份有限公司,2005年。 [22]Gordon S. Linoff及Michael J.A. Berry編著、尹相志譯,資料採礦-網際網路應用與顧客價值管理 Mining the Web,維科圖書有限公司,2004年。
摘要: 
目前大型賣場及百貨公司都會利用資料探勘(Data Mining)技術來進行賣場之商品交易分析,而由於網際網路帶動網路購物之盛行,所以網路購物業者則利用資料探勘(Data Mining)及網路探勘(Web Mining)來進行網站資料、用戶瀏覽行為、以及網路交易分析。
網路探勘(Web Mining)之資料來源,除了交易記錄外,用戶的瀏覽行為資料我們可以從網站之日誌(Log File)中取得,但是如果我們也想利用類似網路探勘(Web Mining)的技術,來分析顧客於實體賣場之購物行走路徑,則最大的問題就是如何取得顧客於賣場購物之行走路徑資料,所以本研究提出以RFID來收集顧客於賣場行走路徑資料之架構。
由於目前要實際在賣場或百貨公司去建置RFID環境來收集顧客行走之路徑資料,就現況而言因為其所需經費不貲,所以在短期內只能先以模擬的方式來進行研究,所以本研究中提出一個可以實際模擬賣場環境及消費行為來產出研究所需資料之資料集產生器(Dataset Generator)。
透過資料集產生器(Dataset Generator)所產出之模擬資料,利用Customer Access Matrix (CAM) 求賣場之用戶喜好購物路徑(Preferred Shopping Path),並以網路交易探勘(Web Transaction Mining)技術求得顧客在賣場上之行走路徑及其商品交易分析。

At present, Data Mining technology can be used in large-scale shopping centers or department stores to help with commodity trading analysis. With the developments of the market of online shopping, online shopping is one of the most active internet activities, therefore Data Mining and Web Mining technology can also using by online shopping entrepreneurs to analyze website data, analyze user browsing behavior, as well as the analyze network transaction.

The source of Web Mining Technology is the customer's browsing record, which can be found in diary log file, but trading record can't be found in diary log file. If using Web Mining technology to analyze customer's purchase behaviors, the most major problem is how to collect the walking path record of custom walking tour through the shopping mall. Therefore the purpose of this research is collects the walking path record of custom walking tour through the shopping mall by using RFID technology.

In current situation, the budget will comes expensive if developing an RFID environment for collects the walking path record of custom walking tour through the shopping mall. Therefore there is only way to reduce the budget, which is use simulation to conducts this research. So this research may realistic simulation of shopping mall's environment and product the data of need by using Data Generation.

Firstly, use Dataset Generator to produce the simulation data, and then use Customer Access Matrix (UAM) to get the user's Preferred Shopping Paths, after that Web Transaction Mining (WTM) can be collects the walking path record of customer walking tour through the shopping mall and commodity trade analysis.
URI: http://hdl.handle.net/11455/19442
其他識別: U0005-1507200715412100
Appears in Collections:資訊科學與工程學系所

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