C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
QARAB: a question answering system to support the Arabic language
SEMITIC '02 Proceedings of the ACL-02 workshop on Computational approaches to semitic languages
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
A Lazy Approach to Associative Classification
IEEE Transactions on Knowledge and Data Engineering
ACN: An Associative Classifier with Negative Rules
CSE '08 Proceedings of the 2008 11th IEEE International Conference on Computational Science and Engineering
Multi-label Classification based on Association Rules with Application to Scene Classification
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
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Associative classification (AC) is a promising data mining approach which builds more accurate classifiers than traditional classification technique such as decision trees and rule induction. By integrating association rules mining with classification, AC has two main phases which are rule generation and classifier building. In this paper, we investigate one of the well known AC algorithm i.e. MCAR, Naïve Bayesian method (NB) and Support Vector Machine algorithm (SVM) on different Arabic data sets. The bases of our comparison are the most popular text evaluation measures. The Experimental results against different Arabic text categorization data sets reveal that MCAR algorithm outperforms the NB and SVM algorithms with regards to all measures.