Journal of Intelligent Information Systems

, Volume 42, Issue 1, pp 47–73 | Cite as

Bayesian analysis of GUHA hypotheses

  • Robert Piché
  • Marko Järvenpää
  • Esko Turunen
  • Milan Šimůnek


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.


Data mining GUHA Contingency table Bayesian statistics 

Mathematics Subject Classifications (2010)

62F15 62H17 62-07 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Robert Piché
    • 1
  • Marko Järvenpää
    • 1
  • Esko Turunen
    • 2
  • Milan Šimůnek
    • 3
  1. 1.Tampere University of TechnologyTampereFinland
  2. 2.Center for Machine Perception, Department of Cybernetics, Faculty of Electrical EngineeringCzech Technical UniversityPragueCzech Republic
  3. 3.University of Economics PraguePragueCzech Republic

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