Experimental Analysis of the Q-Matrix Method in Knowledge Discovery

  • Tiffany Barnes
  • Donald Bitzer
  • Mladen Vouk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)


The q-matrix method, a new method for data mining and knowledge discovery, is compared with factor analysis and cluster analysis in analyzing fourteen experimental data sets. This method creates a matrix-based model that extracts latent relationships among observed binary variables. Results show that the q-matrix method offers several advantages over factor analysis and cluster analysis for knowledge discovery. The q-matrix method can perform fully unsupervised clustering, where the number of clusters is not known in advance. It also yields better error rates than factor analysis, and is comparable in error to cluster analysis. The q-matrix method also allows for automatic interpretation of the data sets. These results suggest that the q-matrix method can be an important tool in automated knowledge discovery.


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  1. 1.
    Barnes, T., Bitzer, D.: Fault tolerant teaching and knowledge assessment: Evaluation of the q-matrix method. In: Proceedings of the 40th ACMSE, Raleigh, NC (April 2003)Google Scholar
  2. 2.
    Birenbaum, M., Kelly, A., Tatsuoka, K.: Diagnosing knowledge state in algebra using the rule-space model. Journal for Research in Mathematics Education 24(5), 442–459 (1993)CrossRefGoogle Scholar
  3. 3.
    Brewer, P.: Methods for concept mapping in computer based education. Computer Science Masters Thesis, North Carolina State University (1996)Google Scholar
  4. 4.
    Elder, J.F., Abbott, D.W.: A comparison of leading data mining tools. In: Proc. 4th Intl. Conf. on Knowledge Discovery and Data Mining (1998)Google Scholar
  5. 5.
    Erlich, Z., Gelbard, R., Spiegler, I.: Data Mining by Means of Binary Representation: A Model for Similarity and Clustering. Information Systems Frontiers 4(2), 187–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Jones, S.: Computer assisted learning of mathematical recursion. Computer Science Masters Thesis, North Carolina State University (1996)Google Scholar
  7. 7.
    Kauffman, L., Rousseeuw, P.J.: Finding groups in data. Wiley, New York (1990)CrossRefGoogle Scholar
  8. 8.
    Kline, P.: An easy guide to factor analysis. Rutledge, London (1994)Google Scholar
  9. 9.
    NovaNET educational network, Online http://www.pearsondigital.com/novanet/
  10. 10.
    Statistical Analysis Software (SAS) and Help System, Online http://www.sas.com
  11. 11.
    Sellers, J.: An empirical evaluation of a fault-tolerant approach to computer-assisted teaching of binary relations. Computer Science Masters Thesis, North Carolina State University (1998)Google Scholar
  12. 12.
    VanLehn, K., Niu, Z., Siler, S., Gertner, A.: Student modeling from conventional test data: A Bayesian approach without priors. Intelligent Tutoring Systems, 434–443 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tiffany Barnes
    • 1
  • Donald Bitzer
    • 2
  • Mladen Vouk
    • 2
  1. 1.Computer Science Dept.University of North Carolina at CharlotteCharlotteUSA
  2. 2.Computer Science Dept.North Carolina State UniversityRaleighUSA

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