What makes patterns interesting in knowledge discovery systems
Inhaltsverzeichnis
Referenz
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE Trans. Knowl. Data Eng. 8(6), 970–974 (1996)
DOI
http://dx.doi.org/10.1109/69.553165
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 the 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
Extended Abstract
Bibtex
@ARTICLE{553165,
author={A. Silberschatz and A. Tuzhilin},
journal={IEEE Transactions on Knowledge and Data Engineering},
title={What makes patterns interesting in knowledge discovery systems},
year={1996},
volume={8},
number={6},
pages={970-974},
keywords={belief maintenance;deductive databases;knowledge acquisition;actionable measure;belief system;discovery process;interestingness measurement;knowledge discovery systems;objective measures;subjective measures;underlying data;unexpected measure;Cities and towns;Costs;Data security;Information systems;Insurance;Medical services;Pattern recognition;State estimation},
doi={10.1109/69.553165},
url={http://dx.doi.org/10.1109/69.553165 http://de.evo-art.org/index.php?title=What_makes_patterns_interesting_in_knowledge_discovery_systems},
ISSN={1041-4347},
month={Dec},
}
Used References
R. Agrawal, T. Imielinsky and A. Swami, "Mining Association Rules Between Sets of Items in Large Databases," Proc. ACM SIGMOD Conf., pp. 207-216, 1993. http://dx.doi.org/10.1145/170035.170072
P. Cheeseman, "In Defense of Probability," Proc. IJCAI Conf., 1985.
R.T. Cox, "On Inference and Inquiry&mdash,An Essay in Inductive Logic," Levine and Tribus, eds., The Maximum Entropy Formalisms, MIT Press, 1979.
V. Dhar and A. Tuzhilin, "Abstract-Driven Pattern Discovery in Databases," IEEE Trans. Knowledge and Data Engineering, vol. 5, no. 6, 1993. http://dx.doi.org/10.1109/69.250075
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.
E.T. Jaynes, Probability Theory: The Logic of Science. Cambridge Univ. Press, to appear. http://dx.doi.org/10.1017/CBO9780511790423
S.K. Kachigan, Statistical Analysis. Radius Press, 1986.
M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen and A.I. Verkamo, "Finding Interesting Rules from Large Sets of Discovered Association Rules," Proc. Third Int',l Conf. Information and Knowledge Management, Dec. 1994. http://dx.doi.org/10.1145/191246.191314
D. Lenat and R.V. Guha, Building Large Knowledge-Based Systems. Addison-Wesley, 1990.
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.
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.
G. Piatetsky-Shapiro and C.J. Matheus, "The Interestingness of Deviations," Proc. AAAI ',94 Workshop Knowledge Discovery in Databases, pp. 25-36, 1994.
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.
P. Smets, "Belief Functions," P. Smets, A. Mamdani, D. Dubois, and H. Prade, eds., Non-Standard Logics for Automated Reasoning, Academic Press, 1988.
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.
J. Ullman, Principles of Database and Knowledge-Base Systems, vol. 1. Computer Science Press, 1988. http://dx.doi.org/10.1109/DASFAA.2003.1192362
Links
Full Text
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.53.2780&rep=rep1&type=pdf
