Abstract
This abstract summarizes key points of our ongoing project that aims at developing several new methods in knowledge discovery in databases (KDD) and implementing them together with related techniques in an interactive-graphic environment. This research lies in several areas if KDD such as supervised classi fication, conceptual clustering, discovery of association rules, parallel and distributed data mining, genetic programming. Some of them are the continuation of our work done so far [1], [2], [3], and some others have been started recently
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References
Nguyen T.D. and Ho T.B., ”An Interactive-Graphic System for Decision Tree Induction”, to appear in Journal of Japanese Society for Artificial Intelligence, January 1999.
Ho T.B., “Discovering and Using Knowledge From Unsupervised Data”, International Journal Decision Support Systems, Elsevier Science, Vol. 21, No. 1, 27–41, 1997.
Ito T., Iba H. and Sato S., “Depth-Dependent Crossover for Genetic Programming”, Proceedings of the 1998 IEEE International Conference on Evolutionary Computation (ICEC’98), 775–780, IEEE Press, 1998.
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© 1998 Springer-Verlag Berlin Heidelberg
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Ho, T.B., Nguyen, T.D., Nguyen, N.B., Ito, T. (1998). Development of Some Methods and Tools for Discovering Conceptual Knowledge. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_48
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DOI: https://doi.org/10.1007/3-540-49292-5_48
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