Abstract
How to increase both autonomy and versatility of a knowledge discovery system is a core problem and a crucial aspect of KDD (Knowledge Discovery and Data Mining). We have been developing a multi-agent based KDD methodology/system called GLS (Global Learning Scheme) for performing multi-aspect intelligent data analysis as well as multi-level conceptual abstraction and learning. With multi-level and multi-phase process, GLS increases versatility and autonomy. This paper presents our recent development on the GLS methodology/system.
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References
Brachman, R.J. and Anand, T. “The Process of Knowledge Discovery in Databases: A Human-Centred Approach”, In Advances in Knowledge Discovery and Data Mining, MIT Press (1996) 37–58.
Engels, R. “Planning Tasks for Knowledge Discovery in Databases-Performing Task-Oriented User-Guidance”, Proc. Second International Conference on Knowledge Discovery and Data Mining (KDD-96), AAAI Press (1996) 170–175.
Fayyad, U.M., Piatetsky-Shapiro, G, and Smyth, P. “From Data Mining to Knowledge Discovery: an Overview”, In Advances in Knowledge Discovery and Data Mining, MIT Press (1996) 1–36.
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (eds.) “Advances in Knowledge Discovery and Data Mining”, AAAI Press (1996).
Kargupta, H. and Chan, P. (eds.) “Advances in Distributed and Parallel Knowledge Discovery”, AAAI Press (2000).
Liu, C. and Zhong, N. “Rough Problem Settings for Inductive Logic Programming”, Zhong, N., Skowron, A., and Ohsuga, S. (eds.) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, LNAI 1711, Springer-Verlag (1999) 168–177.
Michalski, R.S., Kerschberg, L., Kaufman, K.A., and Ribeiro, J.S. “Mining for Knowledge in Databases: The INLEN Architecture, Initial Implementation and First Results”, Journal of Intell. Infor. Sys., Kluwer Academic Publishers, Vol. 1, No. 1 (1992) 85–113.
Nguyen, S.H. and Nguyen, H.S. “Quantization of Real Value Attributes for Control Problems“, Proc. Forth European Congress on Intelligent Techniques and Soft Computing EUFIT’96 (1996) 188–191.
Ohsuga, S. and Yamauchi, H. “Multi-Layer Logic-A Predicate Logic Including Data Structure as Knowledge Representation Language”, New Generation Computing, Vol. 3, No. 4 (1985) 403–439.
Russell, S.J. and Norvig, P. Artificial Intelligence-A Modern Approach Prentice Hall, Inc. (1995).
Piatetsky-Shapiro, G. and Frawley, W.J. (eds.), Knowledge Discovery in Databases, AAAI Press and The MIT Press (1991).
Quinlan, J.R. C4.5: Programs for Machine Learning, Morgan Kaufmann (1993).
Zhong, N. and Ohsuga, S. “Toward A Multi-Strategy and Cooperative Discovery System”, Proc. First International Conference on Knowledge Discovery and Data Mining (KDD-95), AAAI Press (1995) 337–342.
Zhong, N. and Ohsuga, S. “A Hierarchical Model Learning Approach for Refining and Managing Concept Clusters Discovered from Databases”, Data & Knowledge Engineering, Vol. 20, No. 2, Elsevier Science Publishers (1996) 227–252.
Zhong, N. and Ohsuga, S. “System for Managing and Refining Structural Characteristics Discovered from Databases”, Knowledge Based Systems, Vol. 9, No. 4, Elsevier Science Publishers (1996) 267–279.
Zhong, N., Kakemoto, Y., and Ohsuga, S. “An Organized Society of Autonomous Knowledge Discovery Agents”, Peter Kandzia and Matthias Klusch (eds.) Cooperative Information Agents. LNAI 1202, Springer-Verlag (1997) 183–194.
Zhong, N., Liu, C., and Ohsuga, S. “A Way of Increasing both Autonomy and Versatility of a KDD System”, Z.W. Ras and A. Skowron (eds.) Foundations of Intelligent Systems. LNAI 1325, Springer-Verlag (1997) 94–105.
Zhong, N., Liu, C., Kakemoto, Y., and Ohsuga, S. “KDD Process Planning”, Proc. Third International Conference on Knowledge Discovery and Data Mining (KDD-97), AAAI Press (1997) 291–294.
Zhong, N., Liu, C., and Ohsuga, S. “Handling KDD Process Changes by Incremental Replanning”, J. Zytkow and M. Quafafou (eds.) Principles of Data Mining and Knowledge Discovery. LNAI 1510, Springer-Verlag (1998) 111–120.
Zhong, N. Knowledge Discovery and Data Mining, in the Encyclopedia of Microcomputers, Volume 27 (Supplement 6) Marcel Dekker (2001) 93–122.
Zhong, N. and Skowron, A. “A Rough Sets Based Knowledge Discovery Process”, International Journal of Applied Mathematics and Computer Science, Vol. 11, No. 3, Technical University Press, Poland (2001) 101–117.
Zhong, N., Liu, J., Ohsuga, S., and Bradshaw, J. (eds.) Intelligent Agent Technology: Research and Development, World Scientific (2001).
Zhong, N. and Ohsuga, S. Automatic Knowledge Discovery in Larger Scale Knowledge-Data Bases, in C. Leondes (ed.) The Handbook of Expert Systems, Vol. 4, Academic Press (2001) 1015–1070.
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Zhong, N., Matsui, Y., Okuno, T., Liu, C. (2002). Framework of a Multi-agent KDD System. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_51
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DOI: https://doi.org/10.1007/3-540-45675-9_51
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