Discovering case knowledge using data mining

  • S. S. Anand
  • D. Patterson
  • J. G. Hughes
  • D. A. Bell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1394)


The use of Data Mining in removing current bottlenecks within Case-based Reasoning (CBR) systems is investigated along with the possible role of CBR in providing a knowledge management back-end to current Data Mining systems. In particular, this paper discusses the use of Data Mining in two aspects of the MZ system [ANAN97a], namely, the acquisition of cases and discovery of adaptation knowledge. We discuss, in detail, the approach taken to discover cases and outline the methodology to discover adaptation knowledge. For case discovery, a Kohonen network is used to identify initial clusters within the database. These clusters are then analysed using C4.5 and non-unique clusters are grouped to form concepts. A regression tree induction algorithm is then used to ensure that the concepts are rich in information required to predict the dependent variable in the data set. Cases are then chosen from each of the identified concepts as well as outliers in the database. Initial results obtained in the acquisition of cases are presented and analysed. They indicate that the proposed approach achieves a high reduction in the size of the case base.


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  1. [AGRA94]
    R. Agrawal, R. Srikant. Fast Algorithms for Mining Association Rules in Large Databases, Proc. of the 20th Int. Conf. on VLDB, pp. 487–499, Chile, 1994.Google Scholar
  2. [ANAN97]
    S. S. Anand, B. W. Scotney, M. G. Tan, S. I. McClean, D. A. Bell, J. G. Hughes, I. C. Magill. Designing a Kernel for Data Mining, IEEE Expert, pp. 65–74, April, 1997.Google Scholar
  3. [ANAN97a]
    S. S. Anand, W. Dubitzky, D. Patterson, A. Schuster, J. G. Hughes. M2: A First Step Towards Automated Generation and Updating of Case-Knowledge from Databases, Internal Report, Faculty of Informatics, University of Ulster, 1997 (available from http: //iservel. infj. ulst. ac. uk. 8080/m2. ps).Google Scholar
  4. [ASHL88]
    K.D. Ashley, E.L. Rissland. A case-based approach to modelling legal expertise, in IEEE Expert, 3(3), pp. 70–77, 1988.CrossRefGoogle Scholar
  5. [CLEM96]
    CLEMENTINE User Guide, Integral Solutions Ltd, Basingstoke, England, 1996.Google Scholar
  6. [CURE96]
    O. Curet, J. Elliott, M. Jackson. Designing knowledge discovery based systems in business, finance and accounting with a case-based approach: two case studies, IEE Colloquium on Knowledge Discovery and Data Mining, 1996.Google Scholar
  7. [DUB196]
    W. Dubitzky, J. G. Hughes, D. A. Bell. A Generic, Object-Oriented CaseKnowledge Representation Scheme, and its Integration into a Wider Information Management Scenario, in Expert Systems: The International Journal of Knowledge Engineering and Neural Networks, vol. 13 (3), pp. 219–233, Blackwell Publishers, UK, 1996.Google Scholar
  8. [DUB196a]
    W. Dubitzky, J.G. Hughes, D.A. Bell. Case Memory and the Behaviouristic Model of Concepts, in Proc. Advances in Case-Based Reasoning, 3rd European Workshop, EWCBR-96, pp 120–134, Switzerland, 1996.CrossRefGoogle Scholar
  9. [DUB197]
    W. Dubitzky, A. Schuster, J.G. Hughes, D.A. Bell, K. Adamson,. How Similar is VERY YOUNG to 43 Years of Age? On the Representation and Comparison of Polymorphic Properties, 15th International Joint Conference on Artificial Intelligence, Japan, 1997.Google Scholar
  10. [FAYY96]
    U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (Editors). Advances in Knowledge Discovery and Data Mining, AAAI/ MIT Press, 1996.Google Scholar
  11. [GEBH94]
    F. Gebhardt. Discovering interesting statements from a database, Applied Stochastic Models and Data Analysis, Vol. 10, pp. 1–14, 1994.CrossRefGoogle Scholar
  12. [HANN96]
    K. Hanney, M. T. Keane. Learning Adaptation Rules From a Case-Base, in Proc. of European Workshop on Case Based Reasoning, pp. 179–192, 1996.Google Scholar
  13. [MICH83]
    R. S. Michalski. A Theory and Methodology of Inductive Learning, in Machine Learning: An Artificial Intelligence Approach ed. R. S. Michalski, J. G. Carbonell, T. M. Mitchell, pp. 83–134, 1983.Google Scholar
  14. [PAWL95]
    Z. Pawlak, J. Grzymala-Busse, R. Slowinski, and W. Ziarko. Rough Sets, Communications of the ACM, Vol. 38, pp. 89–95, 1995.CrossRefGoogle Scholar
  15. [SHUS97]
    A. Schuster, W. Dubitzky, D.A. Bell, J.G. Hughes, K. Adamson. Aggregating Features and Matching Cases on Vague Linguistic Expressions, 15th International Joint Conference on Artificial Intelligence, Japan, 1997.Google Scholar
  16. [SMIT81]
    E. E. Smith, D. L. Medin, Categories and Concepts, Harvard University Press, Cambridge, Massachusetts, 1981.CrossRefGoogle Scholar
  17. [SMYT95]
    B. Smyth, M. T. Keane. Remembering to Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems, in Proc. of IJCAT-95, pp 337–382, 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • S. S. Anand
    • 1
  • D. Patterson
    • 1
  • J. G. Hughes
    • 1
  • D. A. Bell
    • 2
  1. 1.Northern Ireland Knowledge Engineering LaboratoryUniversity of UlsterNewtownabbey, County AntrimNorthern Ireland
  2. 2.School of Information and Software EngineeringUniversity of UlsterNewtownabbey, County AntrimNorthern Ireland

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