Comparing and Combining Dimension Reduction Techniques for Efficient Text Clustering

Abstract

A great challenge of text mining arises from the increasingly large text datasets and the high dimensionality associated with natural language. In this research, a systematic study is conducted of six Dimension Reduction Techniques (DRT) in the context of the text clustering problem using three standard benchmark datasets. The methods considered include three feature transformation techiques,