Comparing dimension reduction techniques for document clustering

Authors:
Bin Tang;Michael Shepherd;Malcolm I. Heywood;Xiao Luo
Affiliations:
Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada;Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada;Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada;Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
Venue:
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

In this research, a systematic study is conducted of four dimension reduction techniques for the text clustering problem, using five benchmark data sets Of the four methods – Independent Component Analysis (ICA), Latent Semantic Indexing (LSI), Document Frequency (DF) and Random Projection (RP) – ICA and LSI are clearly superior when the k-means clustering algorithm is applied, irrespective of the data sets Random projection consistently returns the worst results, where this appears to be due to the noise distribution characterizing the document clustering task.