Abstract
In this paper we report on an ongoing research project aiming at evaluation of the hypothesis of stabilization of Web user segmentation via cross site information exchange. We check stability of user membership in segments derived at various points of time from the content of sites they visit. If it is true that users of the same service share segments over time that pulling together clustering information over various services may be profitable. If not then the way how users are profiled or clustered needs to be revised.
Chapter PDF
Similar content being viewed by others
References
Antonellis, P., Makris, C., Tsirakis, N.: Algorithms for clustering clickstream data. Preprint Submitted to Information Processing Letters, IPL, October 29 (2007), http://students.ceid.upatras.gr/~tsirakis/publications/Algorithms-for-Clustering-ClickStream-Data-TSIRAKIS.pdf
Ben-David, S., von Luxburg, U., P’al1, D.: A sober look at clustering stability (2006), http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/BenLuxPal06_%5B0%5D.pdf
Ben-Hur, A., Elisseeff, A., Guyon, I.: A stability based method for discovering structure in clustered data. In: Pacific Symposium on Biocomputing (2002)
Bifulco, I., Iorio, F., Napolitano, F., Raiconi, G., Tagliaferri, R.: Interactive visualization tools for meta-clustering. In: Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008, pp. 223–231. IOS Press, Amsterdam (2009), http://dl.acm.org/citation.cfm?id=1564064.1564092
Caruana, R., Elhawary, M., Nguyen, N., Smith, C.: Meta clustering. In: Proceedings of the Sixth International Conference on Data Mining, ICDM 2006, pp. 107–118. IEEE Computer Society, Washington, DC (2006), http://dx.doi.org/10.1109/ICDM.2006.103
Cui, Y., Fern, X.Z., Dy, J.G.: Learning multiple nonredundant clusterings. ACM Transactions on Knowledge Discovery from Data (TKDD) 4, 15:1–15:32 (2010), http://doi.acm.org/10.1145/1839490.1839496
Dasgupta, S., Ng, V.: Which clustering do you want? inducing your ideal clustering with minimal feedback. J. Artif. Int. Res. 39, 581–632 (2010), http://dl.acm.org/citation.cfm?id=1946417.1946430
Ghosh, J., Acharya, A.: Cluster ensembles. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 1(4), 305–315 (2011)
Goder, A., Filkov, V.: Consensus clustering algorithms: Comparison and refinement. In: Munro, J.I., Wagner, D. (eds.) Proceedings of the Workshop on Algorithm Engineering and Experiments, ALENEX 2008, San Francisco, California, USA, January 19, pp. 109–117 (2008), http://www.siam.org/proceedings/alenex/2008/alx08_011godera.pdf
Hore, P., Hall, L.O., Goldgof, D.B.: A scalable framework for cluster ensembles. Pattern Recogn. 42(5), 676–688 (2009), http://dx.doi.org/10.1016/j.patcog.2008.09.027
von Luxburg, U.: Clustering stability: An overview. Foundations and Trends in Machine Learning 2(3), 235–274 (2009)
Niu, D., Dy, J.G., Jordan, M.: Multiple non-redundant spectral clustering views. Proc. ICML 2010 (2010), http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.170.1490&rep=rep1&type=pdf
Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003), http://dx.doi.org/10.1162/153244303321897735
Wei, S., Mirkovic, J., Kissel, E.: Profiling and clustering internet hosts. In: Proc. WorldComp2006 (2006), http://www.isi.edu/~mirkovic/publications/DMI8155.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dramiński, M., Owczarczyk, B., Trojanowski, K., Czerski, D., Ciesielski, K., Kłopotek, M.A. (2013). Stabilization of Users Profiling Processed by Metaclustering of Web Pages. In: Kłopotek, M.A., Koronacki, J., Marciniak, M., Mykowiecka, A., Wierzchoń, S.T. (eds) Language Processing and Intelligent Information Systems. IIS 2013. Lecture Notes in Computer Science, vol 7912. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38634-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-38634-3_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38633-6
Online ISBN: 978-3-642-38634-3
eBook Packages: Computer ScienceComputer Science (R0)