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Bayesian analysis of GUHA hypotheses

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Abstract

The LISp-Miner system for data mining and knowledge discovery uses the GUHA method to comb through a large data base and finds 2 × 2 contingency tables that satisfy a certain condition given by generalised quantifiers and thereby suggest the existence of possible relations between attributes. In this paper, we show how a more detailed interpretation of the data in the tables that were found by GUHA can be obtained using Bayesian statistical methods. Using a multinomial sampling model and Dirichlet prior, we derive posterior distributions for parameters that correspond to GUHA generalised quantifiers. Examples are presented illustrating the new Bayesian post-processing tools implemented in LISp-Miner. A statistical model for the analysis of contingency tables for data from two subpopulations is also presented.

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Authors and Affiliations

  1. Tampere University of Technology, Tampere, Finland

    Robert Piché & Marko Järvenpää

  2. Center for Machine Perception, Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic

    Esko Turunen

  3. University of Economics Prague, Prague, Czech Republic

    Milan Šimůnek

Authors
  1. Robert Piché
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  2. Marko Järvenpää
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  3. Esko Turunen
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  4. Milan Šimůnek
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Correspondence to Robert Piché.

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Piché, R., Järvenpää, M., Turunen, E. et al. Bayesian analysis of GUHA hypotheses. J Intell Inf Syst 42, 47–73 (2014). https://doi.org/10.1007/s10844-013-0255-6

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  • DOI: https://doi.org/10.1007/s10844-013-0255-6

Keywords

Mathematics Subject Classifications (2010)