Best Approximate of Vector Space Model by Using SVD


  • Raghad M. Hadi Departement of Computer Science, College of Science, Mustansiriyah University, IRAQ.



High Dimensional Datasets, Dimensionality reduction, SVD, Vector Space Model.


A quick growth of internet technology makes it easy to assemble a huge volume of data as text document; e. g., journals, blogs, network pages, articles, email letters. In text mining application, increasing text space of datasets represent excessive task which makes it hard to pre-processing documents in efficient way to prepare it for text mining application like document clustering. The proposed system focuses on pre-processing document and reduction document space technique to prepare it for clustering technique. The mutual method for text mining problematic is vector space model (VSM), each term represent a features. Thus the proposed system create vector-space mod-el by using pre-processing method to reduce of trivial data from dataset. While the hug dimen-sionality of VSM is resolved by using low-rank SVD. Experiment results show that the proposed system give better document representation results about 10% from previous approach to prepare it for document clustering


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H. Froud, A. Lachkar and S. A. Ouatik, "Arabic text summarization based on latent semantic analysis to enhance arabic docu-ments clustering," Journal of university sidi mohamed ben abdellah, Morocco, 2012. DOI:

N. S. Pathak, P. P. Rajurkar and A. G. Bhor, "effective approach towards exporter IR system through comparision of various pre-processing techniques," International con-ference on advances in engineering science and management, vol.8, 2015.

N. A. Samat, M. A. Azmi and M. T. Abdul-lah, "Malay documents clustering algorithm based on singular value decomposition," Faculty of computer science and infor-mation technology, university of Putra Ma-laysia, vol.3, 2016.

M. W. Berry, Z. Drma and E. R. Jessuo, "Matrices vector spaces and information retrieval," website www., 2012.

S. Lappin and C. Fox, "Vector space models of lexical meaning," Stephen clark universi-ty of cambridge computer laboratory, vol.25th, 2014.

S. Shama and L. Padmalatha, "Performance comarison of image fusion using singular value decomposition," International journal of innovative research in science, Engineer-ing and technology, vol.4, no.9, 2015. DOI:

D. Munkova, M. Munk and M. Vozar, "Da-ta pre processing evalution for text mining: Transaction/Sequence Model," international conference on computational Science, 2013. DOI:

S. Vijayarani and J. Ilamathi, "Prepro-cessing Techniques for text mining an over-view," International journal of computer science and communication networks, vol.5, 2015.

C. Ramasubramanian, R. Ramya and V. Tamilnadu, "Effective preprocessing activi-ties in text mining using improved porters stemming algorithm," international journal of adanced research in computer and com-munication engineering, vol.2, no.12, 2013.

N. P. Katariya, S. Chaudhari and N. P. Ka-tariya, "Text preprocessing for text mining using side information," international jour-nal of computer science and mobile applica-tion, vol.3, no.1, 2015.




How to Cite

R. M. Hadi, “Best Approximate of Vector Space Model by Using SVD”, Al-Mustansiriyah Journal of Science, vol. 28, no. 2, pp. 143–149, Apr. 2018.



Computer Science