Using Singular Value Decomposition (SVD) as a solution for search result clustering

• Autorzy: Abdulla H. D. , Abdelrahman A. S. , Snasel V.
• Konferencja: Computer Applications in Electrical Engineering 2014 (28-29.04.2014; Poznań, Polska)
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

singular value decomposition; clustering; self-organizing map

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Poznan University of Technology Academic Journals. Electrical Engineering
• Rocznik: 2014
• Tom: No. 80
• Strony: 71--78
• Opis fizyczny: Bibliogr. 8 poz., rys., tab.

Twórcy

autor

Abdulla H. D.

• Technical University of Ostrava

autor

Abdelrahman A. S.

• Technical University of Ostrava

autor

Snasel V.

• Technical University of Ostrava

Bibliografia

• [1] Stanislaw Osinski, “Improving Quality of Search Results Clustering with Approximate Matrix Factorisations”, ECIR 2006.
• [2] Hua-Jun Zeng, Qi-Cai He, Zheng Chen, Wei-Ying Ma, Jinwen Ma, “Learning to cluster web search results”, SIGIR 2004.
• [3] Vasclav Snasel, Petr Gajdos, Hussam Dahwa Abdulla and martin Polovincak, “Concept Lattice Reduction by Matrix Decompositions”, DCCA 2007.
• [4] Vaclav Snasel, Hussam Dahwa Abdulla and Martin Polovincak, “Behavior of the Concept Lattice Reduction to visualizing data after Using Matrix Decompositions”, IEEE Innovations’07, 2007.
• [5] Vasclav Snasel, Martin Polovincak, Hussam Dahwa Abdulla and Zdenek Horak, “On Knowledge Structures Reduction”, IEEE CISIM 2008.
• [6] Václav Snásel, Martin Polovincak, Hussam M. Dahwa Abdulla, Zdenek Horak: On Concept Lattices and Implication Bases from Reduced Contexts. ICCS Supplement 2008.
• [7] M. Berry and M. Browne, “Understanding Search Engines, Mathematical Modelling and Text Retrieval”, Siam, 1999.
• [8] R. M. Larsen, “Lanczos bidiagonalization with partial reorthogonalization”, Technical report, University of Aarhus, 1998.