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ACNB: Associative Classification Mining Based on Naïve Bayesian Method

ACNB: Associative Classification Mining Based on Naïve Bayesian Method

Fadi Odeh, Nijad Al-Najdawi
Copyright: © 2013 |Volume: 8 |Issue: 1 |Pages: 13
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781466630994|DOI: 10.4018/jitwe.2013010102
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MLA

Odeh, Fadi, and Nijad Al-Najdawi. "ACNB: Associative Classification Mining Based on Naïve Bayesian Method." IJITWE vol.8, no.1 2013: pp.23-35. http://doi.org/10.4018/jitwe.2013010102

APA

Odeh, F. & Al-Najdawi, N. (2013). ACNB: Associative Classification Mining Based on Naïve Bayesian Method. International Journal of Information Technology and Web Engineering (IJITWE), 8(1), 23-35. http://doi.org/10.4018/jitwe.2013010102

Chicago

Odeh, Fadi, and Nijad Al-Najdawi. "ACNB: Associative Classification Mining Based on Naïve Bayesian Method," International Journal of Information Technology and Web Engineering (IJITWE) 8, no.1: 23-35. http://doi.org/10.4018/jitwe.2013010102

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Abstract

Integrating association rule discovery and classification in data mining brings a new approach known as associative classification. Associative classification is a promising approach that often constructs more accurate classification models (classifiers) than the traditional classification approaches such as decision trees and rule induction. In this research, the authors investigate the use of associative classification on the high dimensional data in text categorization. This research focuses on prediction, a very important step in classification, and introduces a new prediction method called Associative Classification Mining based on Naïve Bayesian method. The running time is decreased by removing the ranking procedure that is usually the first step in ranking the derived Classification Association Rules. The prediction method is enhanced using the Naïve Bayesian Algorithm. The results of the experiments demonstrate high classification accuracy.

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