Index Based Approach for Text Categorization

Abstract

This research proposes an alternative approach to machine learning based approaches for categorizing online news articles. For using machine learning based approaches for any task of text mining, documents should be encoded into numerical vectors; it causes two problems: huge dimensionality and sparse distribution. Although there are various tasks of text mining such as text categorization, text clustering, and text summarization, the scope of this research is restricted to text categorization. The idea of this research is to avoid the two problems by encoding a document or documents into a table, instead of numerical vectors. Therefore, the goal of this research is to develop a scheme which is free from the two problems for categorizing on-line news article automatically.