Experimental Analysis of the Q-Matrix Method in Knowledge Discovery

  • Tiffany Barnes
  • Donald Bitzer
  • Mladen Vouk
Conference paper

DOI: 10.1007/11425274_62

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3488)
Cite this paper as:
Barnes T., Bitzer D., Vouk M. (2005) Experimental Analysis of the Q-Matrix Method in Knowledge Discovery. In: Hacid MS., Murray N.V., Raś Z.W., Tsumoto S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science, vol 3488. Springer, Berlin, Heidelberg

Abstract

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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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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