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AI
2005
Springer

Comparing Dimension Reduction Techniques for Document Clustering

14 years 5 months ago
Comparing Dimension Reduction Techniques for Document Clustering
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.
Bin Tang, Michael A. Shepherd, Malcolm I. Heywood,
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where AI
Authors Bin Tang, Michael A. Shepherd, Malcolm I. Heywood, Xiao Luo
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