Using a Matrix Decomposition for Clustering Data

Author

Abdulla, Hussam Dahwa ; Polovincak, Martin ; Snasel, Vaclav

Author_Institution

Dept. of Comput. Sci., VSB-Tech. Univ. of Ostrava, Ostrava, Czech Republic

fYear

2009

fDate

24-27 June 2009

Firstpage

18

Lastpage

23

Abstract

There are many search engines in the web and when asked, 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 Matrix Decomposition (Singular Value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF)) can be good solution for the search results clustering.

Keywords

Web services; singular value decomposition; Nonnegative Matrix Factorization; clustering data; matrix decomposition; nonnegative matrix factorizationquery; search engines; singular value decomposition; Computational linguistics; Computer networks; Computer science; Data mining; Information retrieval; Machine learning; Matrix decomposition; Search engines; Singular value decomposition; Social network services; Data clustering; Nonnegative Matrix Factorization (NMF); Search results clustering; Singular Value Decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Aspects of Social Networks, 2009. CASON '09. International Conference on

Conference_Location

Fontainbleu

Print_ISBN

978-1-4244-4613-1

Type

conf

DOI

10.1109/CASoN.2009.11

Filename

5176097