Abstract
We present a rigorous framework, based on optimization, for evaluating data mining operations such as associations and clustering, in terms of their utility in decision-making. This framework leads quickly to some interesting computational problems related to sensitivity analysis, segmentation and the theory of games.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Agrawal, R., Imielinski, T., and Swami, A. 1993. Mining association rules between sets of items in a large database. Proc. ACM SIGMOD Intl. Conference on Management of Data, pp. 207-216.
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A.I. 1996. Fast discovery of association rules. Advances in knowledge discovery and data mining, pp. 307-328, AAAI/MIT Press.
Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. Proc. 20th Intl. Conference on Very Large Databases, pp. 487-499.
Aumann, R. and Hart, S. (Eds.) 1992. Handbook of Game Theory, volume I, Elsevier.
Avriel, M. 1976. Nonlinear Programming: Analysis and Methods. Prentice-Hall.
Brin, S., Motwani, R., and Silverstein, C. 1997a. Beyond market baskets: generalizing association rules to correlations. Proc. ACM SIGMOD Intl. Conference on Management of Data.
Brin, S., Motwani, R., Ullman, J.D., and Tsur, S. 1997b. Dynamic itemset counting and implication rules for market basket data. Proc. ACM SIGMOD Intl. Conference on Management of Data.
Berry, M.J. and Linoff, G. 1997. Data Mining Techniques. John-Wiley.
Chen, M.S., Han, J., and Yu, P.S. 1996. Data mining: An overview from a database perspective. IEEE Trans. on Knowledge and Data Eng., 8(6): 866-884.
Dantzig, G.B. 1963. Linear programming and Extensions. Princeton Univ. Press.
Derman, C. 1970. Finite State Markov Decision Processes. New York: Academic Press.
Gunopoulos, D., Khardon, R., Mannila, H., and Toivonen, H. 1997. Data mining, hypergraph transversals, and machine learning. Proc. ACM SIGACT-SIGMOD Symposium on Principles of Databases Systems, pp. 209-217.
Kleinberg, J., Papadimitriou, C.H., and Raghavan, P. 1998. Segmentation problem. Proc. ACM Symposium on Theory of Computing.
Liu, B. and Hsu, W. 1996. Post-analysis of learned rules. Proc. National Conference on Artificial Intelligence, pp. 828-834.
Masand, B.M. and Piatetsky-Shapiro, G. 1996. A comparison of approaches for maximizing business payoff of prediction models. Proc. Intl. Conference on Knowledge Discovery and Data Mining.
Papadimitriou, C.H. and Steiglitz, K. 1997. Combinatorial Optimization: Algorithms and Complexity, 2nd edition. Dover.
Piatetsky-Shapiro, G. and Matheus, C.J. 1994. The interestingness of deviations. Proc. Intl. Conference on Knowledge Discovery and Data Mining, pp. 25-36.
Silberschatz, A. and Tuzhilin, A. 1996. What makes patterns interesting in knowledge discovery systems. IEEE Trans. on Knowledge and Data Eng., 8(6).
Smyth, P. and Goodman, R.M. 1991. Rule induction using information theory. Proc. Intl. Conference on Knowledge Discovery and Data Mining, pp. 159-176.
Srikant, R. and Agrawal, R. 1995. Mining generalized association rules. Proc. Intl. Conference on Very Large Databases.
Toivonen, H. 1996. Sampling large databases for finding association rules. Proc. Intl. Conference on Very Large Databases.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Kleinberg, J., Papadimitriou, C. & Raghavan, P. A Microeconomic View of Data Mining. Data Mining and Knowledge Discovery 2, 311–324 (1998). https://doi.org/10.1023/A:1009726428407
Issue Date:
DOI: https://doi.org/10.1023/A:1009726428407