Abstract
Portal Catalogs is a popular means of searching for information on the Web. They provide querying and browsing capabilities on data organized in a hierarchy, on a category/subcategory basis. This paper presents mining techniques on user navigational patterns in the hierarchies of portal catalogs. Specifically, we study and implement navigation retrieval methods and clustering tasks based on navigational patterns. The above mining tasks are quite useful for portal administrators, since they can be used to observe users’ behavior, extract personal preferences and re-organize the structure of the portal to satisfy better user needs and navigational habits. These mining tasks have been implemented in the NaviMoz, a prototype system for mining navigational patterns in portal catalogs.
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.
References
Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Transactions on Internet Technology (TOIT) 3(1) (2003)
Anderson, C.R., Horvitz, E.: A Dynamic Personalized Start Page. In: Proceedings of the 11th WWW Conference 2002 (2002)
Ajijth, A., Ramos, V.: Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming. In: Congress on Evolutionary Computation, Canberra, Australia, December 2003, pp. 1384–1391. IEEE Press, Los Alamitos (2003)
Fang, X., Liu Sheng, O.R.: Designing a Better Web Portal for Digital Government: A Web-Mining Based Approach, http://diggov.org/library/library/dgo2005/demosb/fang_designing.pdf
Kaneta, Y., Munna Ahaduzzaman, M.M., Ohkawa, T.: A Method of Extracting Sentences Related to Protein Interaction from Literature using a Structure Database. In: Proceedings of the 2nd European Workshop on Data Mining and Text Mining for Bioinformatics (ECML/PKDD 2004), Italy (September 2004)
Kamdar, T., Joshi, A.: On Creating Adaptive Web Sites using Web Log Mining, TR-CS-00-05. Department of Computer Science and Electrical Engineering University of Maryland, Baltimore Country (2000)
Krishnamurthy, L., Nadeau, J., Ozsoyoglu, G., Ozsoyoglu, M., Schaeffer, G., Tasan, M., Xu, W.: Pathways Database System: An integrated set of tools for biological pathways. Bioinformatics 19(8) (2003)
Mobasher, B., Dai, H., Luo, T., Sung, Y., Zhu, J.: Integrating Web Usage and Content Mining for More Effective Personalization. In: Proceedings of the International Conference on E-Commerce and Web Technologies, Greenwich, UK, pp. 165–176 (2000)
Pensa, R.G., Leschi, C., Besson, J., Boulicaut, J.: Assessment of discretization techniques for relevant pattern discovery from gene expression data. In: Proceedings of the 2nd Workshop on Data Mining in Bioinformatics, Seattle, USA (August 2004)
Pierrakos, D., Paliouras, G., Papatheodorou, C., Karkaletsis, V., Dikaiakos, M.: Web community directories: A new approach to web personalization. In: Berendt, B., Hotho, A., Mladenič, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS, vol. 3209, pp. 113–129. Springer, Heidelberg (2004)
Toolan, F., Kusmerick, N.: Mining web logs for personalized site maps. In: Proceedings of the 3rd International Conference on Web Information Systems Engineering (WISE 2002) (2002)
Wagner, R.A., Fischer, M.J.: The String to String Correction Problem. Journal of the Association for the Computer Machinery 21(1), 168–173 (1974)
Rasmussen, E.: Clustering algorithms. In: Frakes, W., Baeza-Yates, R. (eds.) Information Retrieval: Data Structures and Algorithms. Prentice Hall, Englewood Cliffs (1992)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: Clustering algorithms and validity measures. In: Proceedings of the SSDBM Conference, Virginia, USA (2001)
Dalamagas, T., Cheng, T., Winkel, K.J., Sellis, T.: A Methodology for Clustering XML Documents by Structure. In: Information Systems. Elsevier, Amsterdam (2004)
Hubert, L.J., Levin, J.R.: A general statistical framework for accessing categorical clustering in free recall. Psychological Bulletin 83, 1072–1082 (1976)
Baumgarten, M., Buchner, A.G., Anand, S.S., Mulvenna, M.D., Hughes, J.G.: User-Driven Navigation Pattern Discovery from Internet Data, pp. 74–91
Agrawal, R., Psaila, G., Wimmers, E.L., Zat, M.: Querying shapes of histories. In: Proceedings of 21st International Conference on Very Large Data Bases, pp. 502–514. Morgan Kaufmann, San Francisco (1995)
XML path language, XPath: www.w3.org/TR/xpath
Jardine, N., van Rijsbergen, C.J.: The use of hierarchical clustering in information retrieval. Information storage and retrieval 7, 217–240 (1971)
Voorhees, E.: The effectiveness and efficiency of agglomerative hierarchic clustering in document retrieval, Ph.D. thesis, Cornell University, Ithaca, New York (October 1985)
Hearst, M., Pedersen, J.O.: Reexamining the cluster hypothesis: Scatter/gather on retrieval results. In: Proceedings of the ACM SIGIR Conference, Zurich, Switzerland, pp. 76–84 (1996)
Cormen, T., Leiserson, C., Rivest, R.: Introduction to algorithms. MIT Press, Cambridge (1990)
Gower, J.C., Ross, G.J.S.: Minimum spanning trees and single linkage cluster analysis. Applied Statistics 18, 54–64 (1969)
Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179 (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Christodoulou, E., Dalamagas, T., Sellis, T. (2006). NaviMoz: Mining Navigational Patterns in Portal Catalogs. In: Grust, T., et al. Current Trends in Database Technology – EDBT 2006. EDBT 2006. Lecture Notes in Computer Science, vol 4254. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11896548_60
Download citation
DOI: https://doi.org/10.1007/11896548_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46788-5
Online ISBN: 978-3-540-46790-8
eBook Packages: Computer ScienceComputer Science (R0)