期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2015
卷号：7
期号：1
页码：139
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Classification is an important data mining technique that is used by many applications. Several types ofclassifiers have been described in the research literature. Example classifiers are decision tree classifiers,rule-based classifiers, and neural networks classifiers. Another popular classification technique is naïveBayesian classification. Naïve Bayesian classification is a probabilistic classification approach that usesBayesian Theorem to predict the classes of unclassified records. A drawback of Naïve BayesianClassification is that every time a new data record is to be classified, the entire dataset needs to be scannedin order to apply a set of equations that perform the classification. Scanning the dataset is normally a verycostly step especially if the dataset is very large. To alleviate this problem, a new approach for using naïveBayesian classification is introduced in this study. In this approach, a set of classification rules isconstructed on top of naïve Bayesian classifier. Hence we call this approach Rule-based Naïve BayesianClassifier (RNBC). In RNBC, the dataset is canned only once, off-line, at the time of building theclassification rule set. Subsequent scanning of the dataset, is avoided. Furthermore, this study introduces asimple three-step methodology for constructing the classification rule set.
