Abstract
The everlasting growth of the web in terms of, e.g., amount of information, size of the net, and number of users is demanding for tools that help to tackle content (re)structuring, discover navigation patterns of users, support marketing activities of sellers (e.g., advertising and cross selling), and attract potential customers in this new environment. This paper describes how user navigation paths can be extracted from raw web logfiles and how frequent subsequences can be discovered in those paths. To better cope with navigational behaviour in the large, generalized sequences containing wildcards are used and a new algorithm for mining all frequent generalized subsequences from a given database is presented.
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AGRAWAL, R. and SRIKANT, R. (1994): Fast Algorithms for Mining Association Rules. In: Bocca, J.B., Jarke, M., and Zaniolo, C. (eds.): Proceedings of the 20th International Conference on Very Large Data Bases (VLDB’94), September 12–15, 1994, Santiago de Chile, Morgan Kaufmann, Chile, 487–499.
AGRAWAL, R. and SRIKANT, R. (1995): Mining Sequential Patterns. In: Yu, P.S., and Chen, A.L.P. (eds.): Proceedings of the Eleventh International Conference on Data Engineering, March 6–10, 1995, Taipei, Taiwan, IEEE Computer Society, 3–14.
Bock, H.H. (1974): Automatische Klassifikation, Theoretische und praktische Methoden zur Gruppierung und Strukturierung von Daten (Clusteranalyse), Vandenhoeck Ruprecht, Göttingen.
BORGES, J. and LEVENE, M. (1998): Mining Association Rules in Hypertext Databases. In: Agrawal, R. (ed.): Proceedings/The Fourth International Conference on Knowledge Discovery and Data Mining, August 27–31, 1998, New York, Menlo Park, Calif., 149–153.
BORGES, J. and LEVENE, M. (1999a): Mining Navigation Patterns with Hypertext Probabilistic Grammars. Research Note RN/99/08, Department of Computer Science, University College London, February 1999.
BORGES, J. and LEVENE, M. (1999b): Data Mining of User Navigation Patterns. In: Proceedings of the Workshop on Web Usage Analysis and User Profiling (WEBKDD’99), August 15, 1999, San Diego, CA, Springer, 31–36.
BUECHNER, A.G., BAUMGARTEN, M., ANAND, S.S., MULVENNA, M.D., and HUGHES, J.G. (1999): Navigation Pattern Discovery from Internet Data. In: Proceedings of the Workshop on Web Usage Analysis and User Profiling (WEBKDD’99), August 15, 1999, San Diego, CA, Springer, 25–30.
CHEN, M.-S., PARK, J.S., and YU, P.S. (1996): Data Mining for Path Traversal Patterns in a Web Environment. In: Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS), May 27–30, 1996, Hong Kong, IEEE Computer Society, 385–392.
CHEN, M.-S., PARK, J.S., and YU, P.S. (1998): Efficient Data Mining for Path Traversal Patterns. IEEE Transactions on Knowledge I? Data Engineering 10/2 (1998), 209–221.
COOLEY, R., MOBASHER, B., and SRIVASTAVA, J. (1999a): Web Mining: Information and Pattern Discovery on the World Wide Web. In: 9th International Conference on Tools with Artificial Intelligence (ICTAI ‘87), November 3–8, 1997, Newport Beach, CA.
COOLEY, R., MOBASHER, B., and SRIVASTAVA, J. (1999b): Data Preparation for Mining World Wide Web Browsing Patterns. Journal of Knowledge and Information Systems 1/1 (1999).
GAUL, W. and SCHADER, M. (1999): Data Mining: A New Label for an Old Problem? in: Gaul, W. and Schader, M. (Hrsg.): Mathematische Methoden der Wirtschaftswissenschaft, Festschrift für Otto Opitz, Physica-Verlag, Heidelberg, 3–14.
MOBASHER, B. (2000): Mining Web Usage Data for Automatic Site Personalization. To appear in Studies in Classification, Data Analysis, and Knowledge Organization, 2000.
SHAHABI, C., ZARKESH, A.M., ADIBI, J., and SHAH, V. (1997): Knowledge Discovery from Users Web-Page Navigation. In: 7th International Workshop on Research Issues in Data Engineering (RIDE ‘87), High Performance Database Management for Large-Scale Applications, April 7–8, 1997, Birmingham, UK.
SPILIOPOULOU, M. and FAULSTICH, L.C. (1998): WUM: A Tool for Web Utiliziation Analysis. In: Atzeni, P., Mendelzon, A., and Mecca, G. (eds.): The World Wide Web and Databases, International Workshop WebDB’98, Valencia, Spain, March 27–28, 1998, LNCS 1590, Springer, 184–203.
SPILIOPOULOU, M., FAULSTICH, L.C., and WINKLER, K. (1999): A Data Miner Analyzing the Navigational Behaviour of Web Users. In: Proc. of the Workshop on Machine Learning in User Modelling of the ACAI’99 Int. Conf., Greta, Greece, July 1999.
SPILIOPOULOU, M. (1999): The Laborious Way from Data Mining to Web Mining. Int. Journal of Comp. Sys., Sci. h Eng. 14 (1999), Special Issue on “Semantics of the Web”, 113–126.
SRIKANT, R. and AGRAWAL, R. (1996): Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., and Gardarin, G. (eds.): Advances in Database Technology - EDBT’96, 5th International Conference on Extending Database Technology, Avignon, France, March 25–29, 1996, Proceedings. LNCS 1057, Springer.
VIVEROS, M.S., ELO-DEAN, S., WRIGHT, M.A., and DURI, S.S. (1997): Visitor’s Behaviour: Mining Web Servers. In: Proceedings of the 1st International Conference on the Practical Application of Knowledge Discovery and Data Mining, Blackpool 1997, 257–269.
YAN, T.W., JACOBSEN, M., GARCIA-MOLINA, H., and DAYAL, U. (1996): From User Access Patterns to Dynamic Hypertext Linking. In: Fifth International World Wide Web Conference May 6–10, 1996, Paris, France.
ZAIANE, O.R., XIN, M., and HAN, J. (1998): Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. In: Advances in Digital Libraries, Santa Barbara 1998, 19–29.
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Gaul, W., Schmidt-Thieme, L. (2000). Frequent Generalized Subsequences — A Problem From Web Mining. In: Gaul, W., Opitz, O., Schader, M. (eds) Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58250-9_35
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DOI: https://doi.org/10.1007/978-3-642-58250-9_35
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