Abstract

In this paper we compare usefulness of statistical techniques of dimensionality reduction for improving clustering of documents in Polish. We start with partitional and agglomerative algorithms applied to Vector Space Model. Then we investigate two transformations: Latent Semantic Analysis and Probabilistic Latent Semantic Analysis. The obtained results showed advantage of Latent Semantic Analysis technique over probabilistic model. We also analyse time and memory consumption aspects of these transformations and present runtime details for IBM BladeCenter HS21 machine.

Keywords

MSC

68T50, 68T05, 68T35

Cite this article

@article{cai-12037,
  author  = {Marcin Kuta and Jacek Kitowski},
  title   = {Comparison of Latent Semantic Analysis and Probabilistic Latent Semantic Analysis for Documents Clustering},
  journal = {Computing and Informatics},
  volume  = {33},
  number  = {3},
  pages   = {652--666},
  year    = {2014},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2014/3/comparison-of-latent-semantic-analysis-and-probabilistic-latent-semantic-analysis-for-documents-clustering/}
}
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