Mohammad Rostami - Member of Young Researchers Club, Islamic Azad University, Dehaghan Branch, Isfahan, Iran
Seyed Saeed Ayat - Associate Professor, Department of Computer Engineering and Information Technology, Payame Noor University, Iran
Iman Attarzadeh - Department of Computer Engineering, Islamic Azad University, Dezfoul Branch, Iran
Farid Saghari - MS c, software engineering department

Today a significant part of available data is saved in text database or textdocuments. The most important thing is to organize these documents. One way toorganize text documents is to classify them. To classify texts is to assign textdocuments to their actual categories. This has two main steps, i.e. feature- andlearning algorithm selection. There have been several methods suggested to classifytext documents. In this paper, we propose a combined method to do this moreefficiently. When selecting features, the proposed method uses filtering in order toreduce complexity and it is implemented using naïve Bayes and decision treecategories. Results indicate advantages of this combined method to individualclassifying.

data mining, text documents, classify, decision tree, Bayes