The Research of a Multi-Language Supporting Description-Oriented Clustering Algorithm on Meta-Search Engine Result

Abstract:

Article Preview

Search engine has adopted a variety of techniques to improve the accuracy of information retrieval, but the way of a linear list of search engine results, which mixes unrelated documents with relevant documents, has brought user great burden. This article commits to build clustering of search results, which is based on meta search engine techniques. We use all the popular search engine as a data source, then after a certain pre-processing of the source search engine, hierarchical clustering results is formed and returned to the query users. we propose a multi-language supporting, label first clustering algorithm, which we named DCFC algorithm. This algorithm supports both Chinese and English query, focuses on generating human readable labels, shows search results in hierarchical structure.

Info:

Periodical:

Edited by:

Elwin Mao and Xibing Li

Pages:

549-553

DOI:

10.4028/www.scientific.net/AMM.151.549

Citation:

W. L. Ren and L. J. Liu, "The Research of a Multi-Language Supporting Description-Oriented Clustering Algorithm on Meta-Search Engine Result", Applied Mechanics and Materials, Vol. 151, pp. 549-553, 2012

Online since:

January 2012

Export:

Price:

$38.00

[1] Stanis law Osi´nski, Jerzy Stefanowski, and Dawid Weiss. Lingo: Search results clustering algorithm based on Singular Value Decomposition. In K_lopotek, M.A., Wierzcho' n, S.T., Trojanowski, K., eds.: Proceedings of the International IIS: Intelligent Information Processing and Web Mining Conference. Advances in Soft Computing, Zakopane, Poland, Springer (2004).

DOI: 10.1007/978-3-540-39985-8_37

[2] Chien L.F., PAT-Tree-Based Adaptive Key phrase Extraction for Intelligent Chinese Information Retrieval. In Proceedings of the 20m Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval(SIGIR'93), pages 125-135, Pittsburgh, PA, (1993).

DOI: 10.1145/258525.258534

[3] Lan Huang. A Survey on Web Information Retrieval Technologies [ EB/ OL ] . ECSL Technical Report , State University of New York, (2000).

[4] L Ding, T Finin, A Joshi, R Pan, RS Cost, Y Peng. Swoogle: a search and metadata engine for the semantic web. In CIKM (2005).

[5] Wu L, Mcelean S. Result merging methods in distributed information retrieval with overlapping databases.Information Retrieval, 2007,10(3):297—319.

[6] Carrot2 Framework. Carrot2: Design of a Flexible and Efficient Web Information Retrieval Framework. Third International Atlantic Web Intelligence Conference (AWIC2005), Łodź, Poland, 2005, 439-444.

DOI: 10.1007/11495772_68

[7] P. Ferragina, A. Gulli. A personalized search engine based on web-snippet hierarchical clustering. www14, (2005).

DOI: 10.1145/1062745.1062760

In order to see related information, you need to Login.