Abstract
The increasing number of documents returned by search engines for typical requests makes it necessary to look for new methods of representation of contents of the results, like document maps. Though visually impressive, doc maps (e.g. WebSOM) are extensively resource consuming and hard to use for huge collections.
In this paper, we present a novel approach, which does not require creation of a complex, global map-based model for the whole document collection. Instead, a hierarchy of topic-sensitive maps is created. We argue that such approach is not only much less complex in terms of processing time and memory requirement, but also leads to a robust map-based browsing of the document collection.
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
Boulis, C., Ostendorf, M.: Combining multiple clustering systems. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, Springer, Heidelberg (2004)
Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Proceeding of the International Conference on Artificial Neural Networks 1997, pp. 613–618. Springer, Heidelberg (1997)
Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Journal of Intelligent Information Systems 17(2-3), 107–145 (2001)
Hung, C., Wermter, S.: A constructive and hierarchical self-organising model in a non-stationary environment. In: International Joint Conf. on Neural Networks (2005)
Klopotek, M., Wierzchon, S., Ciesielski, K., Draminski, M., Czerski, D.: Conceptual maps and intelligent navigation in document space (in Polish). Akademicka Oficyna Wydawnicza EXIT Publishing, Warszawa (to appear, 2006)
Kohonen, T., Kaski, S., Somervuo, P., Lagus, K., Oja, M., Paatero, V.: Self-organization of very large document collections, Helsinki University of Technology technical report (2003), http://www.cis.hut.fi/research/reports/biennial02-03
Zhang, T., Ramakrishan, R., Livny, M.: BIRCH: Efficient data clustering method for large databases. In: Proceedings of ACM SIGMOD International Conference on Data Management (1997)
Zhao, Y., Karypis, G.: Criterion functions for document clustering: Experiments and analysis, http://www-users.cs.umn.edu/~karypis/publications/ir.html
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
Ciesielski, K., Kłopotek, M.A. (2006). Contextual Maps for Browsing Huge Document Collections. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_79
Download citation
DOI: https://doi.org/10.1007/11875604_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
eBook Packages: Computer ScienceComputer Science (R0)