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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Comparing Dimension Reduction Techniques for Document Clustering
verfasst von : Bin Tang, Michael Shepherd, Malcolm I. Heywood, Xiao Luo
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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.