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題名 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貢獻者 莊皓鈞<br>周彥君
Chuang, Hao-Chun<br>Chou, Yen-Chun
黃懷萱
Huang, Huai-Hsuan關鍵詞 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 上傳時間 1-Jul-2019 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 資料類型 thesis dc.contributor.advisor 莊皓鈞<br>周彥君 zh_TW dc.contributor.advisor Chuang, Hao-Chun<br>Chou, Yen-Chun en_US dc.contributor.author (Authors) 黃懷萱 zh_TW dc.contributor.author (Authors) Huang, Huai-Hsuan en_US dc.creator (作者) 黃懷萱 zh_TW dc.creator (作者) Huang, Huai-Hsuan en_US dc.date (日期) 2019 en_US dc.date.accessioned 1-Jul-2019 10:46:27 (UTC+8) - dc.date.available 1-Jul-2019 10:46:27 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2019 10:46:27 (UTC+8) - dc.identifier (Other Identifiers) G1063560062 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124135 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 106356006 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 1第二章 文獻探討 3第一節 關聯規則(Association Rule) 3第二節 社群網路分析(Social Network Analysis, SNA) 6第三章 權重設計與社群偵測 9第一節 權重(Weight) 9第二節 社群偵測(Community Detection) 15第四章 研究分析結果 19第一節 資料敘述 19第二節 資料分析結果 21第五章 結論 40參考文獻 43 zh_TW dc.format.extent 4042766 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1063560062 en_US dc.subject (關鍵詞) 購物籃分析 zh_TW dc.subject (關鍵詞) 產品網路分析 zh_TW dc.subject (關鍵詞) 權重 zh_TW dc.subject (關鍵詞) 社群偵測 zh_TW dc.subject (關鍵詞) 社群網路分析 zh_TW dc.subject (關鍵詞) Market basket analysis en_US dc.subject (關鍵詞) Product network analysis en_US dc.subject (關鍵詞) Weight en_US dc.subject (關鍵詞) Community detection en_US dc.subject (關鍵詞) Social network analysis en_US dc.title (題名) 零售購物籃的連結與群集分析 zh_TW dc.title (題名) Analysis of Links and Communities in Retail Market Basket en_US dc.type (資料類型) thesis en_US dc.relation.reference (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. zh_TW dc.identifier.doi (DOI) 10.6814/THE.NCCU.MIS.005.2019.A05 en_US
