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What Makes Patterns Interesting in Knowledge Discovery Systems
December 1996 (vol. 8 no. 6)
pp. 970-974

Abstract—One of the central problems in the field of knowledge discovery is the development of good measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures—those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the subjective measures—those that also depend on the class of users who examine the pattern. The focus of this paper is on studying subjective measures of interestingness. These measures are classified into actionable and unexpected, and the relationship between them is examined. The unexpected measure of interestingness is defined in terms of the belief system that the user has. Interestingness of a pattern is expressed in terms of how it affects the belief system. The paper also discusses how this unexpected measure of interestingness can be used in the discovery process.

[1] R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proc. 1993 ACM-SIGMOD Int'l Conf. Management of Data, pp. 207-216, May 1993.
[2] P. Cheeseman, "In Defense of Probability," Proc. IJCAI Conf., 1985.
[3] R.T. Cox, "On Inference and Inquiry—An Essay in Inductive Logic," Levine and Tribus, eds., The Maximum Entropy Formalisms, MIT Press, 1979.
[4] V. Dhar and A. Tuzhilin, "Abstract-Driven Pattern Discovery in Databases," IEEE Trans. Knowledge and Data Engineering, vol. 5, no. 6, 1993.
[5] W.J. Frawley, G. Piatetsky-Shapiro, and C.J. Matheus, "Knowledge Discovery in Databases: An Overview," G. Piatetsky-Shapiro and W.J. Frawley, eds., Knowledge Discovery in Databases, AAAI/MIT Press, 1991.
[6] E.T. Jaynes, Probability Theory: The Logic of Science. Cambridge Univ. Press, to appear.
[7] S.K. Kachigan, Statistical Analysis. Radius Press, 1986.
[8] M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A.I. Verkamo, “Finding Interesting Rules from Large Sets of Association Rules,” Proc. Third Int'l Conf. Information and Knowledge Management, N.R. Adam, K.B. Bhargava, and Y. Yesha, eds. pp. 401-407, 1994.
[9] D. Lenat and R.V. Guha, Building Large Knowledge-Based Systems. Addison-Wesley, 1990.
[10] C.J. Matheus, G. Piatetsky-Shapiro, and D. McNeill, "An Application of KEFIR to the Analysis of Healthcare Information," Proc. AAAI '94 Workshop Knowledge Discovery in Databases, 1994.
[11] G. Piatetsky-Shapiro, "Discovery, Analysis, and Presentation of Strong Rules," Knowledge Discovery in Databases. G. Piatetsky-Shapiro and W.J. Frawley, eds., AAAI/MIT Press, 1991.
[12] G. Piatetsky-Shapiro and C.J. Matheus, "The Interestingness of Deviations," Proc. AAAI '94 Workshop Knowledge Discovery in Databases, pp. 25-36, 1994.
[13] A. Silberschatz and A. Tuzhilin, "User-Assisted Knowledge Discovery: How Much Should the User Be Involved," Proc. SIGMOD Workshop Research Issues Data Mining and Knowledge Discovery,Montreal, June 1996.
[14] P. Smets, "Belief Functions," P. Smets, A. Mamdani, D. Dubois, and H. Prade, eds., Non-Standard Logics for Automated Reasoning, Academic Press, 1988.
[15] A. Tuzhilin and A. Silberschatz, A Belief-Driven Discovery Framework Based on Data Monitoring and Triggering," Working Paper IS-96-26, Stern School of Business, New York Univ., New York.
[16] J. Ullman, Principles of Database and Knowledge-Base Systems, vol. 1. Computer Science Press, 1988.

Index Terms:
Measures of interestingness, patterns, actionability, unexpectedness, belief systems.
Citation:
Avi Silberschatz, Alexander Tuzhilin, "What Makes Patterns Interesting in Knowledge Discovery Systems," IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 970-974, Dec. 1996, doi:10.1109/69.553165
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