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확장개체모델에서의 학습과 계층파악
Learning and Classification in the Extensional Object Model

김용재    (College of Business Administration, Konkuk University   ); 안준모    (College of Business Administration, Konkuk University   ); 이석준    (College of Business Administration, Konkuk University  );
  • 초록

    Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.


  • 주제어

    Object-oriented Databases .   Machine Learning .   Query Language .   Inheritance .   Classification.  

  • 참고문헌 (30)

    1. Clark, P. and Niblett, T. 'The CN2 Algorithm,' Machine Learning, Vol. 6, No. 4, 1989, pp. 261-283 
    2. Creecy, R., Masand, B., Smith, S., and Waltz, D. 'Trading MIPS and Memory for Knowledge Engineering,' Communications of the ACM, Vol. 35, No. 8, August 1992, pp. 48-64 
    3. Frawley, W., Piatetsky-Shapiro, G., and Matheus, C. 'Knowledge Discovery in Databases: An Overview,' in Proc. First International Conference on Knowledge Discovery and Databases, October 1991, New York 
    4. Li, Q. and McCleod, D. 'Object Flavor Evolution Through Learning in an Object-Oriented Database System,' in Proc. of the 2nd International Conference on Expert Database Systems, L. Kerschberg (ed.), 1989, Benjamin Cummings, Menlo Park, CA, pp. 469-495 
    5. Matheus, C.J., Chan, P.K., and Piatetsky-Shapiro, G., 'Systems for Knowledge Discovery in Databases,' IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 6, December 1993, pp. 903-913 
    6. McCann, J. and Gallagher, J. Expert Systems for Scanner Data Environments, International Series in Quantitative Marketing, Kluwer Academic Publishers, 1990 
    7. Orton, J. and Weick, K. 'Loosely Coupled Systems: A Reconceptualization,' Academy of Management Review, Vol. 15, No. 2, 1990 
    8. Quinlan, J. 'Induction of Decision Trees,' Machine Learning, Vol. 1, 1986, pp. 81-106 
    9. Aha, D., Kibler, D. ani Albert, M 'Instance-Based Learning Algorithms,' Machine Learning, Vol. 6, 1991, pp. 37-66 
    10. Jung, C. A Framework for Computer-Supported Interpretation Systems, Ph.D. Dissertation, The University of Texas at Austin, Department of Management Science and Information Systems, May 1992 
    11. Kim, W. 'Object-Oriented Databases: Definition and Research Directions,' IEEE Transactions on Knowledge and Data Engineering, Vol. 2, No. 3, September 1990 
    12. Mannino, M., Choi, I., and Batory, D.'The Object-Oriented Functional Data Lanaguage,' IEEE Transactions on Software Engineering, Vol. 16, No. 11, November 1990, pp. 1258-1272 
    13. DL86 Daft, R. and Lengel, R. 'Organizational Information Requirements, Media Richness and Structural Design,' Management Science, Vol. 32, No. 5, 1986 
    14. Lalonde, W., Thomas, D., and Pugh, D. 'An Exemplar Based Smalltalk,' in Proc. OOPSLA Conference, October 1986 
    15. Hansen, E. and Widom, J. 'Rule Processing in Active Database Systems,' in Advances in Database and Artificial Intelligence, JAI Press, Greenwich, Connecticut, 1992 
    16. Utgoff, P. 'Incremental Induction of Decision Trees,' Machine Learning, Vol. 4, 1989, Kluwer Academic Publishers, pp. 161-186 
    17. Daft, R. and Weick, C. 'Towards a Model of Organizations as Interpretation Systems,' Academy of Management Review, Vol. 9, No. 2, 1984 
    18. Gennari, J., Langley, P., and Fisher, D. 'Models of Incremental Concept Formation,' Artificial Intelligence, Vol. 40, 1989, pp. 11-61 
    19. Smyth, P. and Goodman, R. 'An Information Theoretic Approach to Rule Induction from Databases,' IEEE Transactions on Knowledge and Data Engineering, Vol. 4, No. 4 (August 1992), pp. 301-316 
    20. Borgida, A., Brachman, R., McGuinness, D., and Resnick, L. 'CLASSIC: A Structural Data Model for Objects,' in Proc. ACM SIGMOD Conference, May 1989, Portland 
    21. Fayyad, U., Piatetsky-Shapiro, G. and Smyth P., 'From Data Mining to Knowledge Discovery in Databases,' AI Magazine, Fall 1996, pp. 37-54 
    22. Clark, P. and Boswell, R. 'Rule Induction with CN2: Some Recent Improvements,' in Proc. Machine Learning - European Working Session on Learning, Porto, Portugal, Springer-Verlag, March 1991, pp. 151-163 
    23. Ceri, S. and Widom, J. 'Deriving Production Rules for Constraint Maintenance,' in Proc. of the Sixteenth International Conf. on Very Large Data Bases, Brisbane, Australia, August 1990, pp. 566-577 
    24. Lieberman, H. 'Using Prototypical Objects to Implement Shared Behavior in Object Oriented Systems,' in Proc. OOPSLA Conference, October 1986 
    25. Goebel M., Le Gruenwald. 'A Survey of Data Mining and Knowledge Discovery Software Tools,' ACM SIGKDD Explorations Newsletter, Vol. 1, 1, 1999, pp. 1-20 
    26. Anthony, M. and Biggs, N. Computational Learning Theory, Cambridge University Press, 1992 
    27. Ioannidis, Y., Saulys, T., D. and Witsitt, A. 'Conceptual Learning in Database Design,' ACM Transactions on Information Systems, Vol. 10, No. 3, July 1992, pp. 265-294 
    28. Borgida, A. and Williamson, K. 'Accommodating Exceptions in Databases and Refining the Schema by Learning from Them,' in Proc. of the 11th International VLDB Conference, August 1985, Stockholm, pp. 72-81 
    29. Rao, Raghav and An, Joon M., 'The effect of team composition on decision scheme, information search, and perceived complexity,' Journal of Organizational Computing and Electronic Commerce, 1995, Vol. 5 Issue 1, pp. 1-20 
    30. Sciore, E. 'Object Specialization,' ACM Transactions on Information Systems, Vol. 7, No. 2, 1989 

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