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Bayesian Data Mining and Knowledge Discovery
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Author(s): Eitel J.M. Lauria (State University of New York, Albany, USA, and Universidad del Salvador, Argentina)and Giri Kumar Tayi (State University of New York, Albany, USA)
Copyright: 2003
Pages: 18
Source title:
Data Mining: Opportunities and Challenges
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-051-6.ch011
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Abstract
One of the major problems faced by data-mining technologies is how to deal with uncertainty. The prime characteristic of Bayesian methods is their explicit use of probability for quantifying uncertainty. Bayesian methods provide a practical method to make inferences from data using probability models for values we observe and about which we want to draw some hypotheses. Bayes’ Theorem provides the means of calculating the probability of a hypothesis (posterior probability) based on its prior probability, the probability of the observations, and the likelihood that the observational data fits the hypothesis. The purpose of this chapter is twofold: to provide an overview of the theoretical framework of Bayesian methods and its application to data mining, with special emphasis on statistical modeling and machine-learning techniques; and to illustrate each theoretical concept covered with practical examples. We will cover basic probability concepts, Bayes’ Theorem and its implications, Bayesian classification, Bayesian belief networks, and an introduction to simulation techniques.
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