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A public domain classification workbench for data mining

Autorzy
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper describes the facilities available in Inducer, a public domain classification workbench aimed at users who wish to analyse their own datasets using a range of data mining strategies or to conduct experiments with a given technique or combination of techniques across a range of datasets. Inducer has a graphical user interface which is designed to be easy to use by beginners, but also includes a range of advanced features for experienced users, including facilities to export the information generated in a form suitable for further processing by other packages. An experiment using the workbench is described.
Czasopismo
Rocznik
Strony
91--102
Opis fizyczny
Bibliogr. 13 poz., wykr.
Twórcy
autor
  • Department of Computer Science and Software Engineering, University of Portsmouth, Lion Terrace, Portsmouth PO1 3HE, United Kingdom, Max.Bramer@port.ac.uk
Bibliografia
  • [1] MLC++ Machine Learning Library in C++ [A library of C++ classes, downloadable from http://www.sgi.com/Technology/mlc].
  • [2] Benbrahim H., Bramer M. A., Impact on Performance of Hypertext Classification of Selective Rich HTML Capture, [in:] Artificial Intelligence Applications and Innovations (Proceedings of AIAI-2004, Toulouse, August 2004), Kluwer, 2004.
  • [3] Blake C. L., Merz C. J., UCI Repository of Machine Learning Databases, University of California, Department of Information and Computer Science, 1998. [http://www.ics.uci.edu/~mlearn/ML Repository.html],
  • [4] Bramer M. A., An Information-Theoretic Approach to the Pre-pruning of Classification Rules, [in:] Intelligent Information Processing, M. Musen, B. Neumann, R. Studer, (eds.), Kluwer, 2002.
  • [5] Bramer M. A., Using J-Pruning to Reduce Overfitting in Classification Trees, Knowledge Based Systems, Vol. 15, No. 5-6, 2002, 301-308.
  • [6] Bramer M. A., Knowledge Web: A Public Domain Expert System Delivery Environment, IEEE International Conference on Systems, Man And Cybernetics, 2003.
  • [7] Cendrowska J., PRISM: An Algorithm for Inducing Modular Rules, International Journal of Man-Machine Studies, Vol. 27, 1987, 349-370.
  • [8] Freitas A. A., On Rule Interestingness Measures, [in:] Research and Development in Expert Systems XV, Springer-Verlag, 1999, 147-158.
  • [9] McSherry D., Explanation of Attribute Relevance in Decision Tree Induction, [in:] Research and Development in Intelligent Systems XVIII, M. A. Bramer, F. Coenen, A. Preece, (eds.), Springer, 2002.
  • [10] Mitchell T., Machine Learning, McGraw-Hill, 1997.
  • [11] POVEL O., Giraud-Carrier C., SwissAnalyst: Data Mining Without the Entry Ticket, [in:] Artificial Intelligence Applications and Innovations (Proceedings of AIAI-2004, Toulouse, August 2004), Kluwer, 2004.
  • [12] Quinlan J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
  • [13] Witten I. H., Frank E., Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2000.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0008-0053
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