Mining the change of customer behavior in dynamic markets

详细信息    查看全文

• 作者：Cheng-Kui Huang ; Ting-Yi Chang ; Badri G. Narayanan
• 关键词：Data mining ; Change mining ; Sequential patterns ; Quantitative data ; Fuzzy sets
• 刊名：Information Technology and Management
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：16
• 期：2
• 页码：117-138
• 全文大小：1,042 KB
• 参考文献：1.Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 1994 international conference very large data bases, 1994, pp 487鈥?99
  2.Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of 1995 international conference data engineering, pp 3鈥?4
  3.Cheung DW, Han J, Ng VT, Wong CY (1996) Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the 12th international conference on data engineering, pp 106鈥?14
  4.Cheung KW, Kwok JT, Law MH, Tsui KC (2003) Mining customer product ratings for personalized marketing. Decis Support Syst 35(2):231鈥?43View Article
  5.Changchien SW, Lee CF, Hsu YJ (2004) On-line personalized sales promotion in electronic commerce. Expert Syst Appl 27(1):35鈥?2View Article
  6.Chen MC, Chiu AL, Chang HH (2005) Mining changes in customer behavior in retail marketing. Expert Syst Appl 28(4):773鈥?81View Article
  7.Chen YL, Huang CK (2005) Discovering fuzzy time-interval sequential patterns in sequence databases. IEEE Trans Syst Man Cybern Part B 35(5):959鈥?72View Article
  8.Chen YL, Huang CK (2006) A new approach for discovering fuzzy quantitative sequential patterns in sequence databases. Fuzzy Sets Syst 157(12):1641鈥?661View Article
  9.Chen YL, Huang CK (2008) A novel knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases. Data Knowl Eng 66(3):349鈥?67View Article
  10.Chen YL, Kuo MH, Wu SY, Tang K (2009) Discovering recency, frequency, and monetary (RFM) sequential patterns from customers鈥?purchasing data. Electron Commer Res Appl 8(5):241鈥?51View Article
  11.Cho YB, Cho YH, Kim SH (2005) Mining changes in customer buying behavior for collaborative recommendations. Expert Syst Appl 28(2):359鈥?69View Article
  12.Dong G, Li J (1998) Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In: Proceedings of 2nd Pacific-Asia conference on research and development in knowledge discovery and data mining, pp72鈥?6
  13.Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the fifth international conference on knowledge discovery and data mining, pp 43鈥?2
  14.Dong L, Laurent A, Poncelet P (2011) WebUser: mining unexpected web usage. Int J Bus Intell Data Min 6(1):90鈥?11View Article
  15.El-Sayed M, Ruiz C, Rundensteiner EA (2004) FS-Miner: efficient and incremental mining of frequent sequence patterns in web logs. In: Proceedings of the 6th annual ACM international workshop on web information and data management, pp 128鈥?35
  16.Fiot C, Laurent A, Teisseire M (2008) Fuzzy sequential pattern mining in incomplete databases. Mathw Soft Comput 15(1):41鈥?9
  17.Fiot C, Laurent A, Teisseire M (2007) From crispness to fuzziness: three algorithms for soft sequential pattern mining. IEEE Trans Fuzzy Syst 15(6):1263鈥?277View Article
  18.Gusfield D (1997) Algorithms on strings, trees, and sequences: computer science and computational biology. Cambridge University Press, CambridgeView Article
  19.Hong TP, Kuo CS, Chi SC (1999) Mining fuzzy sequential patterns from quantitative data. In: The 1999 IEEE international conference on systems, man, and cybernetics, pp 962鈥?66
  20.Han J, Pei J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu MC (2000) FreeSpan: frequent pattern-projected sequential pattern mining. In: Proceedings of 2000 international conference on knowledge discovery and data mining, pp 355鈥?59
  21.Hui SC, Jha G (2000) Data mining for customer service support. Inf Manag 38(1):1鈥?3View Article
  22.Hong TP, Lin CW, Wu YL (2008) Incrementally fast updated frequent pattern trees. Expert Syst Appl 34(4):2424鈥?435View Article
  23.Huang CL, Huang WL (2009) Handling sequential pattern decay: developing a two-stage collaborative recommender system. Electron Commer Res Appl 8(3):117鈥?29View Article
  24.Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice-Hall International Inc., London
  25.Kim JK, Cho YH, Kim WJ, Kim JR, Suh JH (2002) A personalized recommendation procedure for internet shopping support. Electron Commer Res Appl 1(3):301鈥?13View Article
  26.Kim C, Lim JH, Ng R, Shim K (2004) SQUIRE: Sequential pattern mining with quantities. In: Proceedings of the 20th international conference on data engineering, pp 827鈥?27
  27.Lanquillon C (1999) Information filtering in changing domains. In: Proceedings of the international joint conference on artificial intelligence, pp 41鈥?8
  28.Liu B, Hsu W, Han HS, Xia Y (2000) Mining changes for real-life applications. In: Second international conference on data warehousing and knowledge discovery, pp. 337鈥?46
  29.Lin QY, Chen YL, Chen JS, Chen YC (2003) Mining inter-organizational retailing knowledge for an alliance formed by competitive firms. Inf Manag 40(5):431鈥?42View Article
  30.Lin MY, Lee SY (2004) Incremental update on sequential patterns in large databases by implicit merging and efficient counting. Inf Syst 29(5):385鈥?04View Article
  31.Lee CH, Lin CR, Chen MS (2005) Sliding window filtering: an efficient method for incremental mining on a time-variant database. Inf Syst 30(3):227鈥?44View Article
  32.Liu B, Cao SG, He W (2011) Distributed data mining for e-business. Inf Technol Manag 12(2):67鈥?9View Article
  33.Masseglia F, Poncelet P, Teisseire M (2003) Incremental mining of sequential patterns in large databases. Data Knowl Eng 46(1):97鈥?21View Article
  34.Mahdavi I, Cho N, Shirazi B, Sahebjamnia N (2008) Designing evolving user profile in e-CRM with dynamic clustering of Web documents. Data Knowl Eng 65(2):355鈥?72View Article
  35.Matthews SG, Gongora MA, Hopgood AA, Ahmadi S (2012) Temporal fuzzy association rule mining with 2-tuple linguistic representation. In: Proceedings of the 2012 IEEE international conference on fuzzy systems (FUZZ-IEEE 2012), pp 1鈥?
  36.Ng V, Chan S, Lau D, Ying CM (2007) Incremental mining for temporal association rules for crime pattern discoveries. In: Proceedings of the eighteenth conference on Australasian database, pp. 123鈥?32
  37.Padmanabhan B, Tuzhilin A (1999) Unexpectedness as a measure of interestingness in knowledge discovery. Decis Support Syst 27(3):303鈥?18View Article
  38.Pei J, Han J, Mortazavi-Asl B, Zhu H (2000) Mining access patterns efficiently from web logs. In: Proceedings of 2000 Pacific-Asia conference on knowledge discovery and data mining, pp 396鈥?07
  39.Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Dayal U, Hsu M-C (2004) Mining sequential patterns by pattern-growth: the PrefixSpan approach. IEEE Trans Knowl Data Eng 16(11):1424鈥?440View Article
  40.Rabatel J, Bringay S, Poncelet P (2010) Contextual sequential pattern mining. In: 2010 IEEE international conference on data mining workshops, pp 981鈥?88
  41.Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Proceedings of the fifth international conference on extending database technology, pp 3鈥?7
  42.Shaw MJ, Subramaniam C, Tan GW, Welge ME (2001) Knowledge management and data mining for marketing. Decis Support Syst 31(1):127鈥?37View Article
  43.Song HS, Kim JK, Kim SH (2001) Mining the change of customer behavior in an Internet shopping mall. Expert Syst Appl 21(3):157鈥?68View Article
  44.Subramanyam RBV, Goswami A (2005) A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns. Int J Uncertain Fuzziness Knowl Based Syst 13(6):633鈥?52View Article
  45.Tsai CY, Shieh YC (2009) A change detection method for sequential patterns. Decis Support Syst 46(2):501鈥?11View Article
  46.Watson HJ, Frolick MN (1993) Determining information requirement for an EIS. MIS Q 17(3):255鈥?69View Article
  47.Wang K, Yang Q, Yeung JMS (2005) Mining customer value: from association rules to direct marketing. Data Min Knowl Disc 11(1):57鈥?9View Article
  48.Yang TC, Lai H (2006) Comparison of product bundling strategies on different online shopping behaviors. Electron Commer Res Appl 5(4):295鈥?04View Article
  49.Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338鈥?53View Article
  
• 作者单位：Cheng-Kui Huang (1)
  Ting-Yi Chang (1)
  Badri G. Narayanan (2)
  
  1. Department of Business Administration, National Chung Cheng University, 168, University Rd., Min-Hsiung, Chia-Yi, Taiwan, Republic of China
  2. Department of Agricultural Economics, Center for Global Trade Analysis, Purdue University, West Lafayette, IN, USA
  
• 刊物主题：Business Information Systems; Data Structures, Cryptology and Information Theory; Operations Research/Decision Theory; Computer Communication Networks; Business/Management Science, general;
• 出版者：Springer US
• ISSN：1573-7667

文摘

Identification of changes in customer behavior is a major challenge that must be tackled in order to survive in a rapidly changing business environment. For example, because information technology has advanced and data-storage costs have declined, for the purpose of serving customers, numerous enterprises have employed information systems and have directly logged customer behavior in databases. This trend has motivated the development of data mining applications. Fuzzy quantitative sequential pattern mining is a functional data mining technique that is used for discovering customer behavioral patterns over time and determining the quantities of goods or services they purchase. The example term used in shopping 銆圼(Beer, Low)(Milk, High)] (Cola, Middle)銆?means that customers will first buy Beer and Milk in Low and High quantities, respectively,聽and then purchase Cola in Middle quantities on their next shopping trip, where Low, Middle, and High are predefined linguistic terms assigned by managers. A term such as this one provides managers with general and concise knowledge related to customer behavior and allows them to rapidly make decisions in response, especially in a competitive setting. However, literature searches indicate that no previous study has addressed the issue of changes in fuzzy quantitative sequential patterns. The aforementioned example pattern might have been available last year but might not be used this year, and it could have been substituted by 銆?Beer, Middle) {(Cola, Low)(Milk, Low)}銆? If this knowledge is not renewed, managers might develop inappropriate marketing plans for their products or services and use inventory strategies that are outdated with respect to time and quantities. To solve this problem, we propose a novel change-mining model that can be used for detecting changes in fuzzy quantitative sequential patterns. We conducted experiments in which we used real-world and synthetic datasets in order to evaluate the proposed model. When the pattern change was detected using the real-world dataset, the results showed that the model reveals 3 considerations that can help managers with their handling of products鈥?marketing and production. When we studied the model鈥檚 scalability by using the synthetic dataset, the results showed that even though all run times increased when parameter values were decreased, the model remained scalable.