Abstract
Most classification methods are based on the assumption that data conforms to a stationary distribution. The machine learning domain currently suffers from a lack of classification techniques that are able to detect the occurrence of a change in the underlying data distribution. Ignoring possible changes in the underlying concept, also known as concept drift, may degrade the performance of the classification model. Often these changes make the model inconsistent and regular updatings become necessary. Taking the temporal dimension into account during the analysis ofWeb usage data is a necessity, since the way a site is visited may indeed evolve due to modifications in the structure and content of the site, or even due to changes in the behavior of certain user groups. One solution to this problem, proposed in this article, is to update models using summaries obtained by means of an evolutionary approach based on an intelligent clustering approach. We carry out various clustering strategies that are applied on time sub-periods. To validate our approach we apply two external evaluation criteria which compare different partitions from the same data set. Our experiments show that the proposed approach is efficient to detect the occurrence of changes.
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References
Anderberg, M.R.: Cluster analysis for applications. In: Probability and Mathematical Statistics. Academic Press, New York (1973)
Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Journal of Knowledge and Information Systems 1(1), 5–32 (1999)
Da Silva, A., De Carvalho, F., Lechevallier, Y., Trousse, B.: Characterizing visitor groups from web data streams. In: Proceedings of the 2nd IEEE International Conference on Granular Computing (GrC 2006), pp. 389–392, May 10- 12 (2006)
Da Silva, A., De Carvalho, F., Lechevallier, Y., Trousse, B.: Mining web usage data for discovering navigation clusters. In: 11th IEEE Symposium on Computers and Communications (ISCC 2006), pp. 910–915 (2006)
Diday, E., Simon, J.C.: Clustering analysis. In: Fu, K. (ed.) Digital Pattern Classification, pp. 47–94. Springer, Heidelberg (1976)
Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2, 193–218 (1985)
Kosala, R., Blockeel, H.: Web mining research: A survey. ACM SIGKDD Explorations: Newsletter of the Special Interest Group on Knowledge Discovery and Data Mining 2, 1–15 (2000)
Laxman, S., Sastry, P.S.: A survey of temporal data mining. SADHANA - Academy Proceedings in Engineering Sciences, Indian Academy of Sciences 31(2), 173–198 (2006)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: 5th Berkley Symposium on Mathematics and Probability, vol. 1, pp. 281–297 (1967)
Milligan, G.W., Cooper, M.C.: A study of the comparability of external criteria for hierarchical cluster analysis. Multivariate Behavioral Research 21(4), 441–458 (1986)
Roddick, J.F., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 14(4), 750–767 (2002)
Rossi, F., De Carvalho, F., Lechevallier, Y., Da Silva, A.: Comparaison de dissimilarités pour l’analyse de l’usage d’un site web. In: Actes des 6me journes Extraction et Gestion des Connaissances (EGC 2006), Revue des Nouvelles Technologies de l’Information (RNTI-E-6), vol. II, pp. 409–414 (January 2006)
Rossi, F., De Carvalho, F., Lechevallier, Y., Da Silva, A.: Dissimilarities for web usage mining. In: Actes des 10me Confrence de la Fdration Internationale des Socits de Classification (IFCS2006) (July 2006)
Spiliopoulou, M.: Data mining for the web. In: Workshop on Machine Learning in User Modelling of the ACAI 1999, pp. 588–589 (1999)
Tanasa, D., Trousse, B.: Advanced data preprocessing for intersites web usage mining. IEEE Intelligent Systems 19(2), 59–65 (2004)
van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)
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da Silva, A., Lechevallier, Y., Rossi, F., de Carvalho, F. (2009). Clustering Dynamic Web Usage Data. In: Nedjah, N., de Macedo Mourelle, L., Kacprzyk, J. (eds) Innovative Applications in Data Mining. Studies in Computational Intelligence, vol 169. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88045-5_4
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DOI: https://doi.org/10.1007/978-3-540-88045-5_4
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