Czasopismo
2004
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Vol. 59, nr 2,3
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191--201
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Abstrakty
Causal reasoning is important to human reasoning. It plays an essential role in day-to-day human decision-making. Human understanding of causality is necessarily imprecise, imperfect, and uncertain. Soft computing methods may be able to provide the approximation tools needed. In order to algorithmically consider causes, imprecise causal models are needed. A difficulty is striking a good balance between precise formalism and imprecise reality. Determining causes from available data has been a goal throughout human history. Today, data mining holds the promise of extracting unsuspected information from very large databases. The most common methods build rules. In many ways, the interest in rules is that they offer the promise (or illusion) of causal, or at least, predictive relationships. However, the most common rule form (association rules) only calculates a joint occurrence frequency; they do not express a causal relationship. If causal relationships could be discovered, it would be very useful.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
191--201
Opis fizyczny
Bibliogr. 23 poz.
Twórcy
autor
- Applied Artificial Intelligence Laboratory, ECECS Department, University of Cincinnati, Cincinnati, OH 45221, USA, mazlack@UC.edu
Bibliografia
- [1] Asher, H.: Causal Modeling, Sage Publications, Newbury Park, 1983.
- [2] Cooper, G.: A Simple Constraint-Based Algorithm for Efficiently Mining Observational For Causal Relationships, Data Mining and Knowledge Discouvery, 1(2), 1997. 203-224.
- [3] Geiger, D., Heckerman, D.: A Characterization of The Dirichlet Distribution With Application To Learning Bayesian Networks, Proceedings of the 11th Conference on Uncertainty in AI, Montreal, Quebec, August 1995.
- [4] Glymour, C.: The Mind's Arrows, Bayes Nets And Graphical Causal Models In Psychology, MIT Press, Cambridge, Massachusetts, 2001.
- [5] Halpem, P.: The Pursuit Of Destiny, Perseus, Cambridge. Massachusetts, 2000.
- [6] Hatonen, K., Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H.: Knowledge Discovery From Telecommunication Network Alarm Databases, Conference On Data Engineering (ICDE'96) Proceedings, New Orleans, 1996.
- [7] Hausman, D.: Causal Asymmetries, Cambridge University Press, Cambridge, United Kingdom, 1998.
- [8] Heckerman, D., Meek, C., Cooper, G.: A Bayesian Approach To Causal Discovery, Technical Report MSR-TR-97-05. Microsoft, 1997.
- [91 Hobbs, J., Aaronson, L.: Causality, Technical report. Artificial Intelligence Center, SRI, 2000.
- [10] Lakoff, G.: Women, Fire, And Dangerous Things: What Categories Reveal About The Mind, University of Chicago Press, 1990.
- [11] Lederman. L., Teresi, D.: The God Particle: If the Universe Is the Answer, What Is the Question?, Delta. New York, 1993.
- [12] Lewis, D.: Causation and Postscripts to Causation, in: Philosophical Papers, vol. II, Oxford University Press, Oxford. 1986, 172-213.
- [13] Mazlack, L.: Machine Conceptualization Categories, Proceedings 19X7 IEEE Conference on Systems, Man, and Cybernetics, 1987.
- [14] Mellor, D. H.: The Facts of Causation, Routledge, London, 1995.
- [15] Pearl, J.: Causality: Models, Reasoning, And Inference, Cambridge University Press, New York, New York, 2000.
- [16] Pearl, J., Verma, Т.: A Theory of Inferred Causation, Principles Of Knowledge Representation And Reasoning: Proceedings Of The Second International Conference (E. S. J. Allen. R. Fikes, Ed.), Morgan Kaufmann. 1991.
- [17] Shafer, G.: The Art of Causal Conjecture, MIT Press, Cambridge, Massachusetts, 1996.
- [18] Silverstein, C., Brin, S., Motwani, R., Ullman. J.: Scaleable Techniques For Mining Causal Structures, Proceedings 1998 International Conference Very Large Data Bases, NY, 1998.
- [19] Spirtes, P.: An Anytime Algorithm for Causal Inference, Proceedings AI and Statistics 2001, 2001.
- [20] Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, Springer Verlag. New York, 1993.
- [21] Spirtes, P. Glymour, C., Scheines, R.: Causation, Prediction, and Search, second edition. MIT Press, Cambridge, Massachusetts, 2000.
- [22] Suppes, P.: A Probabilistic Theory of Causality, Amsterdam. 1970.
- [23] Zadeh, L.: 2000, Abstract Of A Lecture Presented At The Rolf Nevanilinna Colloquium, University of Helsinki, reported to: Fuzzy Distribution List, fuzzy-mail@dbai.tuwien.ac.at, August 24, 2000.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0005-0010