Turning datamining into a management science tool: New algorithms and empirical results

Management Science

Volumn 46, Issue 2, 2000, Pages 249-264

  Cooper, Lee G ^a   Giuffrida, Giovanni ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; FORECASTING; MARKETING; STRATEGIC PLANNING;

PROMOTION EVENT FORECASTING; RESIDUAL ANALYSIS; RULE GENERATORS;

MANAGEMENT SCIENCE;

EID: 0033885311     PISSN: 00251909     EISSN: None     Source Type: Journal    
DOI: 10.1287/mnsc.46.2.249.11932     Document Type: Article

References (33)

1. Agrawal, R., G. Psaila. 1995. Active data mining. Proc. First Internat. Conf. Knowledge Discovery Data Mining. AAAI Press, Montreal, Canada.
2. _, K. Shim. 1995. Developing tightly-coupled data mining applications on a relational database system: Methodology and experience. IBM Research Report RJ 10005(89094).
3. _, _. 1996. Developing tightly-coupled data mining applications on a relational database system. Proc. Second Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Portland, OR.
4. _, M. Metha, J. Shafer, R. Srikant. 1996b. The Quest data mining system. Proc. Second Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Portland, OR.
5. Anand, T. 1996. The process of knowledge discovery in databases: A human-centered approach. Usama M. Fayyd, Gregory Piatetsky-Shapiro, Padhr Smith, Ramaswamy Uthurusamy, eds. Advances in Knowledge Discovery and Data Mining. AAAI Press/ The MIT Press, Boston, MA.
6. Blattberg, R. C., S. A. Neslin. 1990. Sales Promotion: Concepts, Methods, and Strategies. Prentice-Hall, Englewood Cliffs, NJ.
7. Breiman, L., J. H. Friedman, R. A. Olshen, C. J. Stone. 1984. Classification and Regression Trees. Wadsworth International Group, Belmont, CA.
8. Cheung, D. W., J. Han. 1996. Maintenance of discovered association rules in large databases: An incremental updating technique. Proc. 12th ICDE. New Orleans, LA.
9. Clark, P., T. Niblett. 1989. The CN2 induction algorithm. Machine Learning 3(1) 261-283.
10. Cohen, W. W. 1995. Fast effective rule induction. Proc. Twelfth Internat. Conf. Machine Learning. Lake Tahoe, CA.
11. _. 1996. Learning trees and rules with set-valued features. Proc. Eighth Annual Conf. Innovative Applications of Artificial Intelligence, AAAI-96. Portland, OR.
12. Cooper, L. G., P. Baron, W. Levy, M. Swisher, P. Gogos. (1999) PromoCast@: A new forecasting method for promotion planning. Marketing Sci., 18(3) 301-316.
13. Domingos, P. 1996a. Linear-time rule induction. Proc. Second Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Portland, OR.
14. _. 1996b. Unifying instance-based and rule-based induction. Machine Learning 24 141-168.
15. Fayyad, U. M., G. Piatetsky-Shapiro, P. Smyth, R. Brachman. 1996. From data mining to knowledge discovery: An overview. Usama M. Fayyd, Gregory Piatetsky-Shapiro, Padhr Smith, Ramaswamy Uthurusamy, eds. Advances in Knowledge Discovery and Data Mining. AAAI Press/The MIT Press.
16. Feldman, R., Y. Aumann, A. Amir, H. Mannila. 1997. Efficient algorithms for discovering frequent sets in incremental databases. Proc. 1997 SIGMOD Workshop on DKMD. Tucson, AR.
17. Furkranz, J. 1996. Separate-and-Conquer Rule Learning. Technical Report OEFAI-TR-96-25. Austrian Research Institute for Artificial Intelligence, Vienna, Austria.
18. Hand, D. J. 1997. Construction and Assessment of Classification Rules. John Wiley & Sons, Chichester, UK.
19. Holte, R. C., L. E. Acker, B. W. Porter. 1989. Concept learning and the problem of small disjuncts. Proc. Eleventh Internat. Joint Conf. Artificial Intelligence. Morgan Kaufmann, Detroit, MI.
20. John, G. H., B. Lent. 1997. SIPping from the data firehose. Proc. Third Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Newport Beach, CA.
21. Kakemoto, Y., S. Ohsuga. 1997. KDD process planning. Proc. Third Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Newport Beach, CA.
22. Krycha, K. A. 1999. Case study growmart: The effects of promotional activities on sales. Übung aus Verfahren der Marktforschung Endbericht. Universität Wien, Institute für Betriebswirtschaftslehre, Vienna, Austria.
23. Lilien, G. L., A. Rangaswamy. 1998. Marketing Engineering: Computer-Assisted Marketing Analysis and Planning. Addison-Wesley, Reading, MA.
24. Liu, B., W. Hsu, S. Chen. 1997. Using general impressions to analyze discovered classification rules. Proc. Third Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Newport Beach, CA.
25. Ng, V. T., C. Y. Wong. 1996. Maintainance of discovered association rules in large databases: An incremental updating technique. Proceedings of the 12th ICDE. New Orleans, LA.
26. Piatetsky-Shapiro, G., R. Brachman, T. Khabaza, W. Kloesgen, E. Simoudis. 1996. An overview of issues in developing industrial data mining and knowledge discovery applications. Proc. Second Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Portland, OR.
27. Provost, F. J., V. Kolluri. 1999. A survey of methods for scaling up inductive algorithms. J. Data Mining and Knowledge Discovery 3(2) 131-169.
28. Quinlan, J. R. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco, CA.
29. Rao, V. R., J. H. Steckel. 1998. Analysis for Strategic Marketing. Addison-Wesley, Reading, MA.
30. Shan, N., W. Ziarko. 1995. Data-based acquisition and incremental modification of classification rules. J. Comput. Intelligence 11(2) 357-370.
31. Silberschatz, A., A. Tuzhilin. 1996. What makes patterns interesting in knowledge discovery systems. IEEE Trans. Knowledge Discovery Data Eng. 8(6) 970-974.
32. Thomas, S., S. Bodagala, K. Alsabti, S. Ranka. 1997. An efficient algorithm for the incremental updation of association rules in large databases. Proc. Third Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Newport Beach, CA.
33. Zhong, N., C. Liu. 1997. KDD process planning. Proc. Third Internat. Conf. Knowledge Discovery & Data Mining. AAAI Press, Newport Beach, CA.