Mining and visualising ordinal data with non-parametric continuous BBNs

AM Hanea, Dorota Kurowicka, Roger M Cooke, DA Ababei

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


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. © 2008 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)668-687
Number of pages20
JournalComputational Statistics and Data Analysis
Issue number3
Publication statusPublished - 1 Mar 2010


  • conditional independence statements
  • continuous variables
  • graphical modelling
  • probabilistic modelling
  • probability density function
  • Bayesian networks


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