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Using Singular Value Decomposition as a Solution for Search Result Clustering

  • Hussam D. Abdulla
  • Abdella S. Abdelrahman
  • Václav Snášel
  • Hamoud Aldosari
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)

Abstract

There are many search engines in the web, but they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Singular Value Decomposition (SVD) as a very good solution for search results clustering. Results are presented by visualizing neural network. Neural network is responsive for reducing result dimension to two dimensional space and we are able to present result as a picture that we are able to analyze.

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References

  1. 1.
    Osinski, S.: Improving Quality of Search Results Clustering with Approximate Matrix Factorisations. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 167–178. Springer, Heidelberg (2006)Google Scholar
  2. 2.
    Zeng, H.-J., He, Q.-C., Chen, Z., Ma, W.-Y., Ma, J.: Learning to cluster web search results. In: SIGIR 2004 (2004)Google Scholar
  3. 3.
    Snasel, V., Gajdos, P., Abdulla, H.D., Polovincak, M.: Concept Lattice Reduction by Matrix Decompositions. In: DCCA 2007 (2007)Google Scholar
  4. 4.
    Snasel, V., Abdulla, H.D., Polovincak, M.: Behavior of the Concept Lattice Reduction to visualizing data after Using Matrix Decompositions. In: IEEE Innovations 2007 (2007)Google Scholar
  5. 5.
    Snasel, V., Polovincak, M., Abdulla, H.D., Horak, Z.: On Knowledge Structures Reduction. In: IEEE CISIM (2008)Google Scholar
  6. 6.
    Snasel, V., Polovincak, M., Abdulla, H.D., Horak, Z.: On Concept Lattices and Implication Bases from Reduced Contexts. In: ICCS Supplement (2008)Google Scholar
  7. 7.
    Berry, M., Browne, M.: Understanding Search Engines, Mathematical Modelling and Text Retrieval. Siam (1999)Google Scholar
  8. 8.
    Berry, M., Dumais, S., Letsche, T.: Computation Methods for Intelligent Information Access. In: Proceedings of the 1995 ACM/IEEE Supercomputing Conference (1995)Google Scholar
  9. 9.
    Larsen, R.M.: Lanczos bidiagonalization with partial reorthogonalization. Technical report, University of Aarhus (1998)Google Scholar
  10. 10.
    Osinski, S., Weiss, D.: A Concept-Driven Algorithm for Clustering Search Results. In: IEEE Intelligent System (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hussam D. Abdulla
    • 1
  • Abdella S. Abdelrahman
    • 1
  • Václav Snášel
    • 1
  • Hamoud Aldosari
    • 1
  1. 1.Department of Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic

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