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A Review of Machine Learning Algorithms for Text-Documents Classification

Aurangzeb Khan1, Baharum Baharudin1, Lam Hong Lee2, and Khairullah khan1
1. Department of Computer and Information Science, Universiti Teknologi PETRONAS, Tronoh, Malaysia
2. Faculty of Science, Engineering and Technology, Universiti Tunku Abdul Rahman, Perak Campus, Kampar, Malaysia
Abstract— With the increasing availability of electronic documents and the rapid growth of the World Wide Web, the task of automatic categorization of documents became the key method for organizing the information and knowledge discovery. Proper classification of e-documents, online news, blogs, e-mails and digital libraries need text mining, machine learning and natural language processing techniques to get meaningful knowledge. The aim of this paper is to highlight the important techniques and methodologies that are employed in text documents classification, while at the same time making awareness of some of the interesting challenges that remain to be solved, focused mainly on text representation and machine learning techniques. This paper provides a review of the theory and methods of document classification and text mining, focusing on the existing literature.

Index Terms
— Text mining, Web mining, Documents classification, Information retrieval.

Cite: Aurangzeb Khan, Baharum Baharudin, Lam Hong Lee, and Khairullah khan, "A Review of Machine Learning Algorithms for Text-Documents Classification," Journal of Advances in Information Technology, Vol. 1, No. 1, pp. 4-20, February, 2010.doi:10.4304/jait.1.1.4-20
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