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Mining and visualising ordinal data with non-parametric continuous BBNs


  • Hanea, A.M.
  • Kurowicka, D.
  • Cooke, R.M.
  • Ababei, D.A.


Data mining is the process of extracting and analysing information from large databases. Graphical models are a suitable framework for probabilistic modelling. A Bayesian Belief Net (BBN) is a probabilistic graphical model, which represents joint distributions in an intuitive and efficient way. It encodes the probability density (or mass) function of a set of variables by specifying a number of conditional independence statements in the form of a directed acyclic graph. Specifying the structure of the model is one of the most important design choices in graphical modelling. Notwithstanding their potential, there are only a limited number of applications of graphical models on very complex and large databases. A method for mining ordinal multivariate data using non-parametric BBNs is presented. The main advantage of this method is that it can handle a large number of continuous variables, without making any assumptions about their marginal distributions, in a very fast manner. Once the BBN is learned from data, it can be further used for prediction. This approach allows for rapid conditionalisation, which is a very important feature of a BBN from a user's standpoint.

Suggested Citation

  • Hanea, A.M. & Kurowicka, D. & Cooke, R.M. & Ababei, D.A., 2010. "Mining and visualising ordinal data with non-parametric continuous BBNs," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 668-687, March.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:3:p:668-687

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    References listed on IDEAS

    1. Joe, Harry, 1990. "Multivariate concordance," Journal of Multivariate Analysis, Elsevier, vol. 35(1), pages 12-30, October.
    2. Ross D. Shachter & C. Robert Kenley, 1989. "Gaussian Influence Diagrams," Management Science, INFORMS, vol. 35(5), pages 527-550, May.
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    1. repec:eee:reensy:v:144:y:2015:i:c:p:265-284 is not listed on IDEAS
    2. Hobæk Haff, Ingrid & Aas, Kjersti & Frigessi, Arnoldo & Lacal, Virginia, 2016. "Structure learning in Bayesian Networks using regular vines," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 186-208.
    3. Flaminia Musella, 2013. "A PC algorithm variation for ordinal variables," Computational Statistics, Springer, vol. 28(6), pages 2749-2759, December.
    4. repec:eee:reensy:v:130:y:2014:i:c:p:1-11 is not listed on IDEAS
    5. repec:eee:reensy:v:125:y:2014:i:c:p:153-164 is not listed on IDEAS

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