Abstract
Document representation is an essential step in web page clustering. Web pages are usually written in HTML, offering useful information to select the most important features to represent them. In this paper we investigate the use of nonlinear combinations of criteria by means of a fuzzy system to find those important features. We start our research from a term weighting function called Fuzzy Combination of Criteria (fcc) that relies on term frequency, document title, emphasis and term positions in the text. Next, we analyze its drawbacks and explore the possibility of adding contextual information extracted from inlinks anchor texts, proposing an alternative way of combining criteria based on our experimental results. Finally, we apply a statistical test of significance to compare the original representation with our proposal.
The authors would like to thank the financial support for this research to the Spanish research projects MA2VICMR (S2009/TIC-1542) and Holopedia: the automatic encyclopedia of people and organizations (TIN2010-21128-C02).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dredze, M., Jansen, A., Coppersmith, G., Church, K.: Nlp on spoken documents without asr. In: EMNLP, pp. 460–470 (2010)
Eiron, N., McCurley, K.S.: Analysis of anchor text for web search. In: Proceedings of the 26th SIGIR, pp. 459–460 (2003)
Fresno, V.: Representacion autocontenida de documentos HTML: una propuesta basada en combinaciones heuristicas de criterios. PhD thesis (2006)
Fresno, V., Ribeiro, A.: An analytical approach to concept extraction in html environments. J. Intell. Inf. Syst. 22(3), 215–235 (2004)
Hammouda, K., Kamel, M.: Distributed collaborative web document clustering using cluster keyphrase summaries. Information Fusion 9(4), 465–480 (2008)
Karypis, G.: CLUTO - a clustering toolkit. Technical Report #02-017 (November 2003)
Kosko, B.: Global stability of generalized additive fuzzy systems. IEEE Transactions on Systems, Man, and Cybernetics - C 28, 441–452 (1998)
Liu, Y., Liu, Z.: An improved hierarchical k-means algorithm for web document clustering. In: ICCSIT, September 2-29, pp. 606–610 (2008)
Noll, M.G., Meinel, C.: The metadata triumvirate: Social annotations, anchor texts and search queries. In: Proceedings of the WI-IAT, vol. 1, pp. 640–647 (2008)
Ribeiro, A., Fresno, V., Garcia-Alegre, M.C., Guinea, D.: A fuzzy system for the web page representation (2003)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM (1975)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining (2000)
Tan, Q., Mitra, P.: Clustering-based incremental web crawling. ACM Trans. Inf. Syst. 28, 17:1–17:27 (2010)
Tang, B., Shepherd, M., Milios, E., Heywood, M.I.: Comparing and combining dimension reduction techniques for efficient text clustering. In: Proceedings of the Workshop on Feature Selection for Data Mining, SDM (2005)
Wang, Y., Kitsuregawa, M.: Evaluating contents-link coupled web page clustering for web search results. In: CIKM, pp. 499–506 (2002)
Zubiaga, A., Martínez, R., Fresno, V.: Getting the most out of social annotations for web page classification. In: ACM DocEng, pp. 74–83 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pérez García-Plaza, A., Fresno, V., Martínez, R. (2012). Fuzzy Combinations of Criteria: An Application to Web Page Representation for Clustering. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-28601-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28600-1
Online ISBN: 978-3-642-28601-8
eBook Packages: Computer ScienceComputer Science (R0)