政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/124135
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 109948/140897 (78%)
造访人次 : 46096387      在线人数 : 909
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/124135


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/124135


    题名: 零售購物籃的連結與群集分析
    Analysis of Links and Communities in Retail Market Basket
    作者: 黃懷萱
    Huang, Huai-Hsuan
    贡献者: 莊皓鈞
    周彥君
    Chuang, Hao-Chun
    Chou, Yen-Chun
    黃懷萱
    Huang, Huai-Hsuan
    关键词: 購物籃分析
    產品網路分析
    權重
    社群偵測
    社群網路分析
    Market basket analysis
    Product network analysis
    Weight
    Community detection
    Social network analysis
    日期: 2019
    上传时间: 2019-07-01 10:46:27 (UTC+8)
    摘要: 本研究發展一套應用社群網路分析於零售業的併買分析方法，實際以國內知名便利商店之產品交易資料進行實作。首先，透過不同的權重設計表達產品併買的實際強度及金流，並賦予權重意義。此外，透過社群偵測分析產品的併買關係，若產品間的關係緊密則自成一群，並針對劃分後的社群衡量每一群體的價值與重要性。接著，更進一步以網路中心性為衡量指標找出關鍵產品與橋樑產品。最後，則透過最長鏈結關係，找出產品間除了直接併買關係之外的間接併買關係。透過這一套分析方法能清楚地探討產品交易數據中的併買關係及找出網路中具有價值的社群與產品，以協助零售業者對於交易資料能有更全面的分析與應用，進而制定更完善的決策和推出更貼近消費者需求的行銷策略與活動，提升企業的營收與獲利。
    This research develops a set of techniques for analysis of co-purchased in the retail industry using social network analysis (SNA), and actually implements those techniques on the transaction data from the domestic well-known convenience stores. First, different weighting methods are used to express the actual strengths and cash flows of co-purchased products. In addition, this research uses the community detection to identify co-purchased products from transaction data. If a set of products are frequently purchased together, they form a self-contained community whose internal connections are stronger than external connections. Also, the value and importance of each community will be measured. This research further uses the metrics from the network analysis to identify roles of products among communities. Finally, on top of products purchased together, this research uses the longest link to identify the indirect relationships of co-purchased products. Overall, this research aims to propose an analysis method to better understand co-purchased products from the transaction data. We visually represent co-purchased products using product network, evaluate networks via different weights, and discover relationships among products from the network. The proposed method should help retailers better understand their transaction data, and hence provide information to improve their marketing strategies.
    參考文獻: Agrawal, R., Swami, A., & Imielinski, T. (1993). Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6), 914–925.
    Barnes, J. A. (1954). Class and Committees in a Norwegian Island Parish. Human Relations, 7(1), 39–58.
    Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
    Barrat, A., Barthélemy, M., & Vespignani, A. (2004). Weighted evolving networks: Coupling topology and weight dynamics. Physical Review Letters, 92(22).
    Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), 1–12.
    Brin, S., Motwani, R., & Silverstein, C. (1997). Beyond Market Baskets: Generalizing Association Rules to Correlation. In Proceedings of the 1997 ACM SIGMOD international conference on Management of data (pp. 265–276).
    Bruzzese, D., & Davino, C. (2008). Visual Mining of Association Rules. In Visual Data Mining: Theory, Techniques and Tools for Visual Analytics (pp. 103–122). Berlin, Heidelberg: Springer Berlin Heidelberg.
    Chawla, S., Arunasalam, B., & Davis, J. (2003). Mining Open Source Software (OSS) data using association rules network. In 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 (pp. 461–466).
    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.
    Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 70(6), 6.
    Coleman, J. S. (1990). Foundations of Social Theory. Cambridge MA: Harvard University Press.
    Forte Consultancy Group. (2013). Product Network Analysis – The Next Big Thing in Retail Data Mining. Retrieved from https://forteconsultancy.wordpress.com/2013/02/19/product-network-analysis-the-next-big-thing-in-retail-data-mining/
    Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3), 75–174.
    Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1, 215–239.
    Freeman, L. C. (2000). Visualizing Social Networks. Journal of Social Structure, 1(1).
    Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821–7826.
    Hahsler, M., & Chelluboina, S. (2011). Visualizing Association Rules: Introduction to the R-extension Package arulesViz. R Project Module.
    Hanneman, R. A., & Riddle, M. (2005). Introduction to Social Network Methods. California: University of California, Riverside.
    Hollenbeck, J. R., & Jamieson, B. B. (2015). Human Capital, Social Capital, and Social Network Analysis: Implications for Strategic Human Resource Management. Academy of Management Perspectives, 29(3), 370–385.
    Huang, Z., Chen, H., & Zeng, D. (2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems, 22(1), 116–142.
    Kaur, M., & Kang, S. (2016). Market Basket Analysis: Identify the Changing Trends of Market Data Using Association Rule Mining. In Procedia Computer Science (pp. 78–85).
    Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: A comparative analysis. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 80(5), 056117.
    Li, W., & Schuurmans, D. (2011). Modular community detection in networks. IJCAI International Joint Conference on Artificial Intelligence, 1366–1371.
    Mitchell, J. C. (1969). The concept and use of social networks. In Social networks in urban situations (pp. 1–50).
    Moreno, J. L. (1934). Who Shall Survive? New York: Beacon House.
    Mostafa, M. M. (2015). Knowledge Discovery of Hidden Consumer Purchase Behaviour: A Market Basket Analysis. International Journal of Data Analysis Techniques and Strategies.
    Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 70(5), 9.
    Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 69(2), 026113.
    Pandey, G., Chawla, S., Poon, S., Arunasalam, B., & Davis, J. G. (2009). Association rules network: Definition and applications. Statistical Analysis and Data Mining, 1(4), 260–279.
    Raeder, T., & Chawla, N. V. (2009). Modeling a store’s product space as a social network. In Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009 (pp. 164–169).
    Raeder, T., & Chawla, N. V. (2011). Market basket analysis with networks. Social Network Analysis and Mining, 1(2), 97–113.
    Scott, J. (1991). Social network analysis: A handbook. London: Sage Publications.
    Svetina, M., & Zupančič, J. (2005). How to Increase Sales in Retail with Market Basket Analysis. Systems Integration, 418–428.
    Tang, L., & Liu, H. (2010). Community Detection and Mining in Social Media. Morgan & Claypool Publishers.
    Tewari, A. S., Kumar, A., & Barman, A. G. (2014). Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. In Souvenir of the 2014 IEEE International Advance Computing Conference, IACC 2014 (pp. 500–503).
    Verma, N., & Orlando, S. (2017). Market Basket Analysis with Network of Products.
    Videla-Cavieres, I. F., & Ríos, S. A. (2014). Extending market basket analysis with graph mining techniques: A real case. Expert Systems with Applications, 41(4 PART 2), 1928–1936.
    Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. New York: Cambridge University Press.
    Xie, J., Zhu, W., & Wang, K. (2014). An Improvement to E-Commerce Recommendation Using Product Network. Pacis 2014 Proceedings, 182.
    Xu, J., & Chen, H. (2005). Criminal network analysis and visualization. Communications of the ACM, 48(6), 100–107.
    Zinoviev, D., Zhu, Z., & Li, K. (2015). Building mini-categories in product networks. Studies in Computational Intelligence, 597, 179–190.
    描述: 碩士
    國立政治大學
    資訊管理學系
    106356006
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G1063560062
    数据类型: thesis
    DOI: 10.6814/THE.NCCU.MIS.005.2019.A05
    显示于类别:[資訊管理學系] 學位論文

    文件中的档案:

    档案 大小格式浏览次数
    006201.pdf3948KbAdobe PDF20检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈