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2006
Springer

Improving Quality of Search Results Clustering with Approximate Matrix Factorisations

10 years 4 months ago
Improving Quality of Search Results Clustering with Approximate Matrix Factorisations
Abstract. In this paper we show how approximate matrix factorisations can be used to organise document summaries returned by a search engine into meaningful thematic categories. We compare four different factorisations (SVD, NMF, LNMF and K-Means/Concept Decomposition) with respect to topic separation capability, outlier detection and label quality. We also compare our approach with two other clustering algorithms: Suffix Tree Clustering (STC) and Tolerance Rough Set Clustering (TRC). For our experiments we use the standard merge-thencluster approach based on the Open Directory Project web catalogue as a source of human-clustered document summaries.
Stanislaw Osinski
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ECIR
Authors Stanislaw Osinski
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