Abstract
It is generally convinced that pre-processing for data mining is needed to exclude irrelevant and meaningless aspects of data before applying data mining algorithms. From this viewpoint, we have already proposed a notion of Information Theoretical Abstraction, and implemented a system ITA. Given a relational database and a family of possible abstractions for its attribute values, called an abstraction hierachy ITA selects the best abstraction among the possible ones so that class distributions needed to perform our clasification task are preserved as possibly as we can. According to our previous experiment, just one application of abstraction for the whole database has shown its effectiveness in reducing the size of detected rules, without making the classification error worse. However, as C4.5 performs serial attribute-selection repeatedly, ITA does not generally guarantee the preservingness of class distributions, given a sequence of attribute-selections. For this reason, in this paper, we propose a new version of ITA, called iterative ITA, so that it tries to keep the class distributions in each attribute selection step as possibly as we can.
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References
Adriaans, P. and Zantinge, D.: Data Mining, Addison Wesley Longman Ltd., 1996.
Arimoto, S: Probability, Information, Entropy. Morikita Shuppan, 1980 (in Japanese).
Fayyad, U.N., Piatctsky-Sliapiro, G., Smyth, P. and Uthurusamy, R.(eds.): Advances in Knowledge Discovery and Data Mining. AAAIIMIT Press, 1996.
Fayyad, U.N., Piatctsky-Shaprio, G., Srriytli, P.: From Data Mining to Knowledge Discovery: an Overview. In [3], pp.1–33
Han, J. and Fu, Y.: Attribute-Oriented Induction in Data Mining. In [3], pp.399–421
Holsheimer, M. and Kersten, M: Architectural Support for Data Mining, In: CWI Technical Report CS-R9429, Amsterdam, The Netherlands, 1994.
Kudoh, Y. and Haraguchi, M.: An Appropriate Abstration for an Attribute-Oriented Induction. Proceeding of The Second International Conferencc on Dis-covcry Science, LNAI 1721, pp.43–65, 1999.
Kudoh, Y. and Haraguchi, M.: Data Mining by Generalizing Database Based on ail Appropriatc Abstraction, In: Journal of Japanese Society for Artificial Intelligence, vol.15, No.4, July, pp.638–648, 2000 (in Japanese).
Kudoh, Y. and Haraguchi, M.: An Appropriate Abstraction for Constructing a Compact Decision Tree, Proceegin of The Third International Conference on Discovery Science, LNAI, (to appear), 2000.
Matsumoto, K., Morita, C. and Tsukimoto, H. Generalized Rule Discovery in Databases by Finding Similarities In: SIG-J-9401-15, pp.111–118, Japanese Society for Artificial Intelligence, 1994.
Miclialski, R.S., Bratko, I. and Kubat, M. (eds.): Machine Learning and Data Mining: Methods arid Applications, London, John Wiley & Sons, 1997.
Miclialski, R.S. and Kaufman, K.A.: Data Mining and Knowledge Discovery: A Reivew of Issues and a Multistrategy ApproachIn: [11] pp.71–112, 1997.
Miller, G. A., Beckwith, R., Fellbaum, C., Gross, D. and Miller, K.: Introduction to WordNet: A n On-line Lexical DataBase In: Intcernational Journal of Lexicography 3 (4), pp.235–244, 1990.
Miller, G.A.: Nouns in WordNet: a lexical inheritance system, In: International Journal of Lexicography 3 (4), pp. 245–264, 1990. ftp://ftp.cogsci.princeton.edu/pub/wordnet/5papers.ps
Murphy, P.M. and Aha, D.W.: UCI Repository of machine learning databases, http://www.ics.uci.edu/ mlearn/MLRepository.html.
Quinaln, J.R.: C4.5-Programs for Machine Learning, Morgan Kaufmann, 1993.
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Kudoh, Y., Haraguchi, 1. (2000). Detecting a Compact Decision Tree Based on an Appropriate Abstraction. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_10
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