TY  - JOUR
T1  - Text Categorization using Association Rule and Na?ve Bayes Classifier
AU - , S. M. Kamruzzaman AU - , Chowdhury Mofizur Rahman 
JO  - Asian Journal of Information Technology
VL  - 3
IS  - 9
SP  - 657
EP  - 665
PY  - 2004
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2004.657.665
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2004.657.665
KW  - 
AB  - As the amount of online text increases, the demand for text categorization to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic categorization of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. Text categorization using Association Rule and Na?ve Bayes Classifier is proposed here. Instead of using words word relation i.e association rules from these words is used to derive feature set from pre-classified text documents. Na?ve Bayes Classifier is then used on derived features for final categorization.
ER  -