Comparing and Combining Dimension Reduction Techniques for Efficient Text Clustering

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, Independent Component Analysis (ICA), Latent Semantic Indexing (LSI), Random Projection (RP), and three feature selection techniques based on Document Frequency (DF ), mean TfIdf (TI) and Term Frequency Variance (TfV ). Experiments with the k-means clustering algorithm show that ICA and LSI are clearly superior to RP on all three datasets. Furthermore,it is shown that TI and TfV outperform DF for text clustering. Finally, experiments where a selection technique is followed by a transformation technique show that this combination can help substantially reduce the computational cost associated with the best transformation methods (ICA and LSI) while preserving clustering performance.