Application of Data Mining Using Bayesian Belief Network To Classify Quality of Web Services

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Executive Summary

In this paper, the authors employed Na?ve Bayes, Augmented Na?ve Bayes, Tree Augmented Na?ve Bayes, Sons & Spouses, Markov Blanket, Augmented Markov Blanket, Semi Supervised and Bayesian network techniques to rank web services. The Bayesian Network is demonstrated on a dataset taken from literature. The dataset consists of 364 web services whose quality is described by 9 attributes. Here, the attributes are treated as criteria, to classify web services. From the experiments, they conclude that Na?ve based Bayesian network performs better than other two techniques comparable to the classification done in literature.

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