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From Knowledge Discovery to Computational Intelligence: A Framework for Intelligent Decision Support Systems

  • Chapter
Intelligent Decision-making Support Systems

Part of the book series: Decision Engineering ((DECENGIN))

Abstract

The research described in this chapter is concerned with investigating the combination of knowledge discovery in database and intelligent computing technologies, in developing a framework for intelligent decision support systems (IDSS). In this context, the chapter presents an approach for IDSS through the combination of data mining (DM) technology with artificial neural networks (NN) in a hybrid architecture called the DM-NN model. This research draws from the concepts of computational intelligence, knowledge discovery in databases and decision support.

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References

  • Agrawal R, Imielinsk T, Swami A (1993) Mining association rules between sets of items in large databases. ACM SIGMOD Conference on Management of Data (SIGMOD’93), (pp. 207–216). New York, NY: ACM Press.

    Google Scholar 

  • Azvine B, Azarmi N, Nauck D (2000) Intelligent Systems and Soft Computing: Prospects, Tools and Applications. (Eds. Carbonell, J. and Siekmann, J.), Lecture Notes in Artificial Intelligence. Adastral Park, UK: Springer-Verlag.

    Google Scholar 

  • Beckenkamp FG (2002) A Component Architecture for Artificial Neural Network Systems. Faculty of Sciences, Department of Computer and Information Science, University of Constance, Germany. Ph.D. Thesis.

    Google Scholar 

  • Bonczek RH, Holsapple CW, Whinston AB (1981) Foundations of Decision Support Systems. New York: Academic Press.

    Google Scholar 

  • Burstein F, Smith H, Sowunmi A, Sharma R (1998) Organisational Memory Information Systems: a Case-based Approach to Decision Support. In (Eds, Kersten, G., Mikolajuk, Z., Rais, M. and Yeh, A.), Decision Analysis and Support for Sustainable Development, (Chapter 20).

    Google Scholar 

  • Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (1996) From data mining to knowledge discovery: an overview. In Advances in Data Mining and Knowledge Discovery, (pp. 1–34) Cambridge: AAAI/The MIT Press.

    Google Scholar 

  • Fayyad UM, Mannila H, Ramakrishman R (1997) Data Mining and Knowledge Discovery. Boston: Kluwer Academic Publishers.

    Google Scholar 

  • Goonatilake S, Khebbal S (1995) Intelligent hybrid systems: issues, classifications and future directions. In (Eds, Goonatilake, S. and Khebbal, S.), Intelligent Hybrid Systems, (pp. 3–20). New York NY: John Wiley & Sons.

    Google Scholar 

  • Han J (1998) Data Mining: An Overview from Databases Perspective. Tutorial on the Pacific-Asia Conference in Knowledge Discovery and Data Mining (PKDD-98), Melbourne, Australia. April 1998.

    Google Scholar 

  • Hand D, Mannila H, Smyth P (2001) Principles of data mining. (Ed, Dietterich, T.), Adaptive Computation and Machine Learning Series. Cambridge: The MIT Press.

    Google Scholar 

  • Hayes-Roth, F. and Jacobstein, N. (1994). The state of knowledge-based systems. Communications of the ACM, 37(3); 27–39.

    Article  Google Scholar 

  • Holtzman S (1989) Intelligent Decision Systems. (Eds, Buchanan, B., Davis, R., Erman, L.D., King, D., McDermott, J. and Stefik, M.), The Teknowledge Series in Knowledge Engineering. Menlo Park California: Addison-Wesley.

    Google Scholar 

  • Keith R (1991) Results and Recommendations Arising From An Investigation Into Forecasting Problems At Melbourne Airport. Townsville, Australia, Bureau of Meteorology, March 1991. Meteorological Note 195.

    Google Scholar 

  • Kolodner J (1993) Case-based Reasoning. Mountain View, CA: Morgan Kaufmann.

    Google Scholar 

  • Leao BF, Rocha A (1990) Proposed methodology for knowledge acquisition: a study on congenital heart disease diagnosis. Methods of Information in Medicine, 29:30–40.

    Google Scholar 

  • Leao BF, Reategui EB (1993) A hybrid connectionist expert system to solve classificational problems. Proceedings of Computers in Cardiology. London: IEEE Computer Society.

    Google Scholar 

  • Lenat DB, Prakash M, Shepherd M (1986). Cyc: using common sense knowledge to overcome brittleness and knowledge-acquisition bottlenecks. AI Magazine, Winter 1986, 6: 65–85.

    Google Scholar 

  • Machado RJ, Rocha AF (1990) The combinatorial neural network: a connectionist model for knowledge based systems. In (Ed, Bouchon-Meunier, B., Yager, R.R. & Zadeh, L.A.), Uncertainty in knowledge bases. Berlin, Germany: Springer Verlag.

    Google Scholar 

  • Machado RJ, Rocha AF (1992). A hybrid architecture for fuzzy connectionist expert systems. In (Ed, Kandel, A. & Langholz, G.), Hybrid Architectures for Intelligent Systems (pp. 135–152). Boca Raton FL: CRC Press.

    Google Scholar 

  • Machado RJ, Barbosa C, Neves A (1998) Learning in the combinatorial neural model. IEEE Transactions on Neural Networks, 9(5): 831–847

    Article  Google Scholar 

  • Medsker LR (1995) Hybrid Intelligent Systems. Kluwer Academic Publishers.

    Google Scholar 

  • Miller RA (1986) Internist-I: an experimental computer-based diagnostic consultant for general internal medicine. In (Eds, Reggia, J.A. and Stanley, T.), Computer-assisted medical decision-making, 2, 139–158. New York: Springer Verlag.

    Google Scholar 

  • Pree W, Beckenkamp FG, Rosa S.I.V. (1997) Object-oriented design & implementation of a flexible software architecture for decision support systems. Proceedings of the 9th. International Conference on Software Engineering & Knowledge Engineering (SEKE’97), (pp. 382–388). Madrid, Spain. June 1997.

    Google Scholar 

  • Quinlan JR (1993) C4.5: Programs for Machine Learning. San Mateo, CA.: Morgan Kaufmann.

    Google Scholar 

  • Reategui EB, Campbell J (1995) A classification system for credit card transactions. Advances in Case-Based Reasoning: Second European Workshop (EWCBR-94), (pp. 280–291). Chantilly, France. Berlin, Germany: Springer Verlag.

    Google Scholar 

  • Reategui EB (1997) Combining Case-Based Reasoning with Neural Networks in Diagnostic Systems. Computer Science Dept., University of London, UK. PhD Thesis.

    Google Scholar 

  • Schank RC (1982) Dynamic Memory, a Theory of Understanding and Learning in Computers and People. Cambridge UK: University Press.

    Google Scholar 

  • Shim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C (2002) Past, present, and future of decision support technology. Decision Support Systems, 33(2): 111–126.

    Article  Google Scholar 

  • Simon HA (1977) The New Science of Management Decisions. New Jersey, NJ: Prentice Hall.

    Google Scholar 

  • Sprague RH (1993) A framework for the development of decision support systems. In (Eds. Sprague, R.H. and Watson, H.J.), Decision Support Systems Putting Theory into Practice, (pp. 3–26). New Jersey, NJ: Prentice-Hall International.

    Google Scholar 

  • Sun R (2001) Artificial intelligence: connectionist and symbolic approaches. In (Eds. Smelser, N.J. and Baltes, P.B.), International Encyclopedia of the Social and Behavioral Sciences, (pp. 783–789). Oxford England: Pergamon/Elsevier.

    Google Scholar 

  • Tecuci G, Kodratoff Y (1995) Machine Learning and Knowledge Acquisition: Integrated Approaches. London UK: Academic Press.

    Google Scholar 

  • Teng JTC, Mirani R, Sinha A (1988). A unified architecture for intelligent DSS. In proceedings of the 21st Annual Hawaii International Conference on System Sciences (HICSS-21), (pp. 286–294). Hawaii USA: IEEE Computer Society Press.

    Google Scholar 

  • Turban E, Aronson J (1998) Decision Support Systems and Intelligence Systems. 5th Edn., New Jersey: Prentice-Hall.

    Google Scholar 

  • Turban E, Aronson J, Liang T (2005). Decision Support Systems and Intelligence Systems. 7th Edn., New Jersey: Pearson Prentice-Hall.

    Google Scholar 

  • Viademonte S, Leao BF, Hoppen N (1995) Hybrid model for classification expert system. XXI Latin America Conference on Computer Science, (pp. 639–648). Canela, Brazil.

    Google Scholar 

  • Viademonte S, Burstein F, Dahni R, Williams S (2001) Discovering knowledge from meteorological databases: a meteorological aviation forecast study. Third International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2001), (pp. 61–70). Munich, Germany. Berlin, Germany: Springer-Verlag.

    Google Scholar 

  • Viademonte S, Burstein F (2001) An intelligent decision support model for aviation weather forecasting. Advances in intelligent data analysis: 4th international conference (IDA 2001), (pp. 278–288). Cascais, Portugal. Berlin, Germany: Springer-Verlag.

    Google Scholar 

  • Viademonte S (2004) A Hybrid Model for Intelligent Decision Support: Combining Data Mining And Artificial Neural Networks. Faculty of Information Technology, Monash University, Australia. PhD. Thesis.

    Google Scholar 

  • Wang J (1994) Artificial neural networks versus natural neural networks: A connectionist paradigm for preference assessment. Decision Support Systems, 11(5): 415–429.

    Article  Google Scholar 

  • Weiss SM, Indurkhya N (1998) Predictive Data Mining: A Practical Guide. (Ed, Morgan, M.B.), San Francisco, CA USA: Morgan Kaufmann Publishers, Inc.

    Google Scholar 

  • Wu X (1995) Knowledge Acquisition from Databases. Norwood, NJ: Ablex Publishing.

    Google Scholar 

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Authors and Affiliations

  1. School of Information Management & Systems, Monash University, Caulfield Campus, 26 Sir John Monash Drive, Caulfield East, Victoria, Australia

    Sergio Viademonte & Frada Burstein

Authors
  1. Sergio Viademonte
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  2. Frada Burstein
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© 2006 Springer-Verlag London Limited

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Viademonte, S., Burstein, F. (2006). From Knowledge Discovery to Computational Intelligence: A Framework for Intelligent Decision Support Systems. In: Intelligent Decision-making Support Systems. Decision Engineering. Springer, London. https://doi.org/10.1007/1-84628-231-4_4

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  • DOI: https://doi.org/10.1007/1-84628-231-4_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-228-7

  • Online ISBN: 978-1-84628-231-7

  • eBook Packages: EngineeringEngineering (R0)

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