The Research of Fuzzy Classification Rules Applied to CRM
In the era of great competition, understanding and satisfying
customers- requirements are the critical tasks for a company
to make a profits. Customer relationship management (CRM) thus
becomes an important business issue at present. With the help of
the data mining techniques, the manager can explore and analyze
from a large quantity of data to discover meaningful patterns and
rules. Among all methods, well-known association rule is most
commonly seen. This paper is based on Apriori algorithm and uses
genetic algorithms combining a data mining method to discover fuzzy
classification rules. The mined results can be applied in CRM to
help decision marker make correct business decisions for marketing
strategies.
[1] C.A., "Application of Data Warehouse for CRM," Information and
Computer, Vol. 240, pp. 54-57, 2000.
[2] Cardozo, R. N., "An Experimental Study of Customer Effort, Expectation
and Satisfaction," Journal of Marketing Research, Vol. 2, No. 3, pp. 244-
249, 1965.
[3] S.M. Chen and W.T. Jong, "Fuzzy query translation for relational database
system", IEEE Transactions on System, Man, and Cybernetics, Vol. 27,
no. 4, pp. 714-721.
[4] D.E. Goldberg, Genetic Algrithms in Search, Optimization, and Machine
Learning, Addison-Wesley, MA, 1989.
[5] J.W. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan
Kaufmann, San Francisco, 2001.
[6] Y.C. Hu, R.S. Chen and G.H. Tzeng, "Mining fuzzy association rules for
classification problems," Computers and Industrial Engineering, Vol. 43,
No. 4, pp.735-750, 2002.
[7] Y.C. Hu, R.S. Chen and G.H. Tzeng, "Finding fuzzy classification rules
using data mining techniques," Pattern Recognition Letters, Vol. 24,
pp.509-519, 2003.
[8] Y.C. Hu, "Finding useful fuzzy concepts for pattern classification using
genetic algorithm", Information Sciences, Vol.175, pp.1-19, 2005.
[9] H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka, "Selecting fuzzy
if-then rules for classification problems using genetic algorithm", IEEE
Transactions on Fuzzy Systems, Vol. 3, no. 3, pp.260-270, 1995.
[10] H. Ishibuchi, T. Nakashima, T. Murata, "Performance evaluation of fuzzy
classifier systems for multidimensional pattern classification problems",
IEEE Transactions on Systems, Man, and Cybernetics, Vol. 29, no. 5,
pp.601-618, 1999.
[11] Ishibuchi, H., Yamamoto, T., Nakashima, T., "Fuzzy data mining: effect
of fuzzy discretization," In: Proceedings of the 1st IEEE International
Conference on Data Mining, San Jose, USA, pp.241-248, 2001a.
[12] Ishibuchi, H., Nakashima, T., Yamamoto, T., "Fuzzy association rules
for handling continuous attributes," In: Proceedings of IEEE International
Symposium on Industrial Electronics, Pusan, Korea, pp.118-121, 2001b.
[13] Jones, T. O. and W. E. Jr. Sasser, "Why Satisfied Customer Defect,"
Harvard Business Review, Vol. 73, No. 6, pp. 88-99, 1995.
[14] J.S.R. Jang, "ANFIS: adaptive-network-based fuzzy inference systems",
IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3,
pp.665-685, 1993.
[15] J.J. Lyu and Y.C. Lin, "Strategy application of CRM," Quality Control
Journal, Vol. 37, No. 3, pp.20-23, 2001.
[16] T.C. Lin, "Customer Relationship Management (CRM) Research Frameworks
and Some Essential Research Issues," Journal of Information
Management, Vol 9, pp.31-56, 2002.
[17] Y.C. Liu, From ERP, SCM, CRM to EC, DrMarketing Co., Ltd, 2002.
[18] NCR, "Integrating Business Strategy and CRM," e-Business Executive
Report, Nov. pp.20-25, 1999.
[19] Nozaki, K., Ishibuchi, H., Tanaka, H., "Adaptive fuzzy rule-based
classification system," IEEE Transaction on Fuzzy Systems, Vol. 4, NO.
3, pp.238-250, 1996.
[20] W. Pedrycz, F. Gomide, An Introduction of Fuzzy Sets: Analysis and
Design, MIT Press, Cambridge, 1998.
[21] Rooij, A.J.F., Jain, L., Johnson, R.P., Neural Network Training Using
Genetic Algirhtms, World Scientific, Singapore, 1996.
[22] Spreng, R. A., "A Comprehensive Model of the Consumer Satisfaction
Formation Process," Dissertation Abstracts International, Vol. 53, No.7,
pp.2461-2462, 1993.
[23] Swift, R., Accelerating Customer Relationships, Prentice Hall, 2001.
[24] Tiwana, A., The Essential Guide to Knowledge Management, Prentice
Hall PTR, Upper Saddle River, NJ, 2000.
[25] Wang, L.X., Mendel, J.M., "Generating fuzzy rules by learning from
examples", IEEE Transactions on Systems, Man, and Cybernetics, Vol.
22, No.3, pp. 1414-1427, 1992.
[26] Wirtz, Jochen and John E. G. Bateson, "An Experimental Investigation of
Halo Effects in Satisfaction Measures of Service Attributes," International
Journal of Service Industry Management, No.6, pp.84-102, 1995.
[27] Yuan Y. and Shaw, M.J., "Induction of Fuzzy Decision Trees," Fuzzy
Sets and Systems, Vol. 69, pp.125-139, 1995.
[28] L.A. Zadeh, "Fuzzy sets", Information Control, Vol.8 No.3, pp.338-353,
1965.
[29] L.A. Zadeh, "The concept of a linguistic variable and its application
to approximate reasoning", Information Science(part 1), Vol.8, No.3,
pp.199-249, 1975a.
[30] L.A. Zadeh, "The concept of a linguistic variable and its application
to approximate reasoning", Information Science(part 2), Vol.8, No.4,
pp.301-357, 1975b.
[31] L.A. Zadeh, "The concept of a linguistic variable and its application to
approximate reasoning", Information Science(part 3), Vol.9, No.1, pp.43-
80, 1976.
[32] H. -J. Zimmermann, Fuzzy sets, Decision Making, and Expert Systems,
Kluwer, Boston, 1991.
[1] C.A., "Application of Data Warehouse for CRM," Information and
Computer, Vol. 240, pp. 54-57, 2000.
[2] Cardozo, R. N., "An Experimental Study of Customer Effort, Expectation
and Satisfaction," Journal of Marketing Research, Vol. 2, No. 3, pp. 244-
249, 1965.
[3] S.M. Chen and W.T. Jong, "Fuzzy query translation for relational database
system", IEEE Transactions on System, Man, and Cybernetics, Vol. 27,
no. 4, pp. 714-721.
[4] D.E. Goldberg, Genetic Algrithms in Search, Optimization, and Machine
Learning, Addison-Wesley, MA, 1989.
[5] J.W. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan
Kaufmann, San Francisco, 2001.
[6] Y.C. Hu, R.S. Chen and G.H. Tzeng, "Mining fuzzy association rules for
classification problems," Computers and Industrial Engineering, Vol. 43,
No. 4, pp.735-750, 2002.
[7] Y.C. Hu, R.S. Chen and G.H. Tzeng, "Finding fuzzy classification rules
using data mining techniques," Pattern Recognition Letters, Vol. 24,
pp.509-519, 2003.
[8] Y.C. Hu, "Finding useful fuzzy concepts for pattern classification using
genetic algorithm", Information Sciences, Vol.175, pp.1-19, 2005.
[9] H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka, "Selecting fuzzy
if-then rules for classification problems using genetic algorithm", IEEE
Transactions on Fuzzy Systems, Vol. 3, no. 3, pp.260-270, 1995.
[10] H. Ishibuchi, T. Nakashima, T. Murata, "Performance evaluation of fuzzy
classifier systems for multidimensional pattern classification problems",
IEEE Transactions on Systems, Man, and Cybernetics, Vol. 29, no. 5,
pp.601-618, 1999.
[11] Ishibuchi, H., Yamamoto, T., Nakashima, T., "Fuzzy data mining: effect
of fuzzy discretization," In: Proceedings of the 1st IEEE International
Conference on Data Mining, San Jose, USA, pp.241-248, 2001a.
[12] Ishibuchi, H., Nakashima, T., Yamamoto, T., "Fuzzy association rules
for handling continuous attributes," In: Proceedings of IEEE International
Symposium on Industrial Electronics, Pusan, Korea, pp.118-121, 2001b.
[13] Jones, T. O. and W. E. Jr. Sasser, "Why Satisfied Customer Defect,"
Harvard Business Review, Vol. 73, No. 6, pp. 88-99, 1995.
[14] J.S.R. Jang, "ANFIS: adaptive-network-based fuzzy inference systems",
IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3,
pp.665-685, 1993.
[15] J.J. Lyu and Y.C. Lin, "Strategy application of CRM," Quality Control
Journal, Vol. 37, No. 3, pp.20-23, 2001.
[16] T.C. Lin, "Customer Relationship Management (CRM) Research Frameworks
and Some Essential Research Issues," Journal of Information
Management, Vol 9, pp.31-56, 2002.
[17] Y.C. Liu, From ERP, SCM, CRM to EC, DrMarketing Co., Ltd, 2002.
[18] NCR, "Integrating Business Strategy and CRM," e-Business Executive
Report, Nov. pp.20-25, 1999.
[19] Nozaki, K., Ishibuchi, H., Tanaka, H., "Adaptive fuzzy rule-based
classification system," IEEE Transaction on Fuzzy Systems, Vol. 4, NO.
3, pp.238-250, 1996.
[20] W. Pedrycz, F. Gomide, An Introduction of Fuzzy Sets: Analysis and
Design, MIT Press, Cambridge, 1998.
[21] Rooij, A.J.F., Jain, L., Johnson, R.P., Neural Network Training Using
Genetic Algirhtms, World Scientific, Singapore, 1996.
[22] Spreng, R. A., "A Comprehensive Model of the Consumer Satisfaction
Formation Process," Dissertation Abstracts International, Vol. 53, No.7,
pp.2461-2462, 1993.
[23] Swift, R., Accelerating Customer Relationships, Prentice Hall, 2001.
[24] Tiwana, A., The Essential Guide to Knowledge Management, Prentice
Hall PTR, Upper Saddle River, NJ, 2000.
[25] Wang, L.X., Mendel, J.M., "Generating fuzzy rules by learning from
examples", IEEE Transactions on Systems, Man, and Cybernetics, Vol.
22, No.3, pp. 1414-1427, 1992.
[26] Wirtz, Jochen and John E. G. Bateson, "An Experimental Investigation of
Halo Effects in Satisfaction Measures of Service Attributes," International
Journal of Service Industry Management, No.6, pp.84-102, 1995.
[27] Yuan Y. and Shaw, M.J., "Induction of Fuzzy Decision Trees," Fuzzy
Sets and Systems, Vol. 69, pp.125-139, 1995.
[28] L.A. Zadeh, "Fuzzy sets", Information Control, Vol.8 No.3, pp.338-353,
1965.
[29] L.A. Zadeh, "The concept of a linguistic variable and its application
to approximate reasoning", Information Science(part 1), Vol.8, No.3,
pp.199-249, 1975a.
[30] L.A. Zadeh, "The concept of a linguistic variable and its application
to approximate reasoning", Information Science(part 2), Vol.8, No.4,
pp.301-357, 1975b.
[31] L.A. Zadeh, "The concept of a linguistic variable and its application to
approximate reasoning", Information Science(part 3), Vol.9, No.1, pp.43-
80, 1976.
[32] H. -J. Zimmermann, Fuzzy sets, Decision Making, and Expert Systems,
Kluwer, Boston, 1991.
@article{"International Journal of Information, Control and Computer Sciences:51454", author = "Chien-Hua Wang and Meng-Ying Chou and Chin-Tzong Pang", title = "The Research of Fuzzy Classification Rules Applied to CRM", abstract = "In the era of great competition, understanding and satisfying
customers- requirements are the critical tasks for a company
to make a profits. Customer relationship management (CRM) thus
becomes an important business issue at present. With the help of
the data mining techniques, the manager can explore and analyze
from a large quantity of data to discover meaningful patterns and
rules. Among all methods, well-known association rule is most
commonly seen. This paper is based on Apriori algorithm and uses
genetic algorithms combining a data mining method to discover fuzzy
classification rules. The mined results can be applied in CRM to
help decision marker make correct business decisions for marketing
strategies.", keywords = "Customer relationship management (CRM), Data mining,
Apriori algorithm, Genetic algorithm, Fuzzy classification rules.", volume = "6", number = "5", pages = "577-6", }
