Abstract
The advancement of knowledge discovery from databases (KDD) has been hampered by the problems such as the lack of statistical rigor, overabundance of patterns, and poor integration. This paper describes a new model for KDD that applies a causal network to guide the discovery processes. The new model not only allows the user to express what kind of knowledge to be discovered, but also uses the user intention to alleviate the overabundance problem. In this new model, the causal network is applied to represent the relevant variables and their relationships in the problem domain, and in due course updated according to the extracted knowledge. An interactive data mining process based on this model is described. The approach allows a knowledge discovery process to be conducted in a more controllable manner. Fundamental features of the new model are discussed, and an example is provided to illustrate the discovery processes using this model.
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References
R. L. Blum, “Induction of causal relationships from a time-oriented clinical database: An overview of RX project,” Proceedings of Second National Conference on Artificial Intelligence, MIT Press, Cambridge, MA, pp.355–357, 1982.
N. Cercone, and M. Tsuchiya (Guest eds.), 1993, Special issue on Learning and Discovery in knowledge-based databases, IEEE Trans. Knowl. Data Eng., Vol. 5, No. 6, 1993.
G. F. Cooper and E. Herskovits, “A Bayesian method for the induction of probabilistic networks from data,” Machine Learning, Vol. 9, No. 4, pp. 309–348, 1994,.
R. G. Cowell, A. P. Dawid, and D. J. Spiegelhalter, “Sequential model criticism in probabilistic expert systems,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 3, pp. 209–219, 1993.
J. Han, Y. Cai and N. Cercone, Data-driven discovery of quantitative rules in relational databases, IEEE Trans. Knowledge and Data Engineering, Vol. 5, No. 1, pp. 29–40, 1993.
E. Herskovits and G. F. Cooper, “Kutato: An entropy-driven system for construction of probabilistic expert systems from databases,” Uncertainty in Artificial Intelligence, Amsterdam, North Holland, pp. 117–125, 1991
J. Pearl, Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, Palo Alto, CA, Second printing, 1991
G. Piatetsky-Shapiro, and W. J. Frawley (eds.), Knowledge Discovery in Databases, Menlo Park, CA: ALBRIGHT UNIV.AI/MIT Press, 1991
G. Piatetsky-Shapiro, et, al, “KDD-93: Progress and challenges in knowledge discovery in databases,”AI Magazine, Vol. 15, No. 3, pp. 77–82, 1994
M. Provan and J. R. Clarke, “Dynamic network construction and updating techniques for the diagnosis of Acute Abdominal Pain, ” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 3, pp. 299–307, 1993
S. K. M. Wong and P. Lingras, “Representation of qualitative user preference by quantitative belief functions,” IEEE Transactions on Knowledge and Data Engineering, Vol. 6, No. 1, pp. 72–78, 1993. *** DIRECT SUPPORT *** A0008166 00009
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© 1997 Springer-Verlag Berlin Heidelberg
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Zhu, Q., Chen, Z. (1997). Knowledge discovery from databases with the guidance of a causal network. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1997. Lecture Notes in Computer Science, vol 1325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63614-5_39
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DOI: https://doi.org/10.1007/3-540-63614-5_39
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