Mining and visualising ordinal data with non-parametric continuous BBNs

Hanea, AM and Kurowicka, Dorota and Cooke, Roger M and Ababei, DA (2010) Mining and visualising ordinal data with non-parametric continuous BBNs. Computational Statistics and Data Analysis, 54 (3). pp. 668-687. (https://doi.org/10.1016/j.csda.2008.09.032)

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Abstract

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.

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