Latest Research Papers On Web Usage Mining

  • 1.

    consortium on discovering knowledge with Inductive Queries (cInQ). Project funded by the European Commission under the Information Society Technologies Programme (1998-2002) Future and Emerging Technologies arm. Contract no. IST-2000-26469, http://www.cinq-project.org Bibliography on Web Usage Mining, available at http://www.cinq-project.org/intranet/polimi/

  • 2.

    Configuration File of W3C httpd (1995), http://www.w3.org/Daemon/User/Config/

  • 3.

    W3C Extended Log File Format (1996), http://www.w3.org/TR/WD-logfile.html

  • 4.

    Accrue (2003), http://www.accrue.com

  • 5.

    Funnel Web Analyzer (2003), http://www.quest.com

  • 6.

    NetIQ WebTrends Log Analyzer (2003), http://www.netiq.com

  • 7.

    Sane NetTracker (2003), http://www.sane.com/products/NetTracker

  • 8.

    WebSideStory HitBox (2003), http://www.websidestory.com

  • 9.

    WUM: A Web Utilization Miner (2003), http://wum.wiwi.hu-berlin.de

  • 10.

    Adomavicius, G., Tuzhilin, A.: Extending recommender systems: A multidimensional approachGoogle Scholar

  • 11.

    Andersen, J., Giversen, A., Jensen, A.H., Larsen, R.S., Pedersen, T.B., Skyt, J.: Analyzing clickstreams using subsessions. In: International Workshop on Data Warehousing and OLAP, DOLAP 2000 (2000)Google Scholar

  • 12.

    Corin, R.A.: A Machine Learning Approach to Web Personalization. PhD thesis, University of Washington (2002)Google Scholar

  • 13.

    Ansari, S., Kohavi, R., Mason, L., Zheng, Z.: Integrating ecommerce and data mining: Architecture and challenges. In: WEBKDD 2000 - Web Mining for E-Commerce – Challenges and Opportunities, Second International Workshop (August 2000)Google Scholar

  • 14.

    Ansari, S., Kohavi, R., Mason, L., Zheng, Z.: Integrating e-commerce and data mining: Architecture and challenges. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM 2001). IEEE Computer Society, Los Alamitos (2001)Google Scholar

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    Banerjee, A., Ghosh, J.: Clickstream clustering using weighted longest common subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining (2001)Google Scholar

  • 16.

    Berendt, B.: Using site semantics to analyze, visualize, and support navigation. Data Mining and Knowledge Discovery 6(1), 37–59 (2002)CrossRefMathSciNetGoogle Scholar

  • 17.

    Berendt, B., Mobasher, B., Nakagawa, M., Spiliopoulou, M.: The impact of site structure and user environment on session reconstruction in web usage analysis. In: Proceedings of the 4th WebKDD 2002 Workshop, at the ACM-SIGKDD Conference on Knowledge Discovery in Databases (KDD 2002) (2002)Google Scholar

  • 18.

    Borges, J.: A Data Mining Model to Capture UserWeb Navigation Patterns. PhD thesis, Department of Computer Science University College London (2000)Google Scholar

  • 19.

    Bounsaythip, C., Rinta-Runsala, E.: Overviewof data mining for customer behavior modeling. Technical Report TTE1-2001-18, VTT Information Technology (2001)Google Scholar

  • 20.

    Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1–7), 107–117 (1998)CrossRefGoogle Scholar

  • 21.

    Shiu, S., Wong, C., Pal, S.: Mining fuzzy association rules for web access case adaptation. In: Case-Based Reasoning Research and Development: Proceedings of the Fourth International Conference on Case-Based Reasoning (2001)Google Scholar

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    Catledge, L.D., Pitkow, J.E.: Characterizing browsing strategies in the World-Wide Web. Computer Networks and ISDN Systems 27(6), 1065–1073 (1995)CrossRefGoogle Scholar

  • 23.

    Chang, C.-Y., Chen, M.-S.: A new cache replacement algorithm for the integration of web caching and prefectching. In: Proceedings of the eleventh international conference on Information and knowledge management, pp. 632–634. ACM Press, New York (2002)CrossRefGoogle Scholar

  • 24.

    Chen, M., LaPaugh, A.S., Singh, J.P.: Predicting category accesses for a user in a structured information space. In: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 65–72 (2002)Google Scholar

  • 25.

    Cooley, R.: Web Usage Mining: Discovery and Application of Interesting Patterns from Web Data. PhD thesis, University of Minnesota (2000)Google Scholar

  • 26.

    Cooley, R., Mobasher, B., Srivastava, J.: Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems 1(1), 5–32 (1999)Google Scholar

  • 27.

    Diebold, B., Kaufmann, M.: Usage-based visualization of web localities. In: Australian symposium on Information visualisation, pp. 159–164 (2001)Google Scholar

  • 28.

    Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Transactions on Internet Technology (TOIT) 3(1), 1–27 (2003)CrossRefGoogle Scholar

  • 29.

    Etzioni, O.: The world-wide web: Quagmire or gold mine? Communications of the ACM 39(11), 65–68 (1996)CrossRefGoogle Scholar

  • 30.

    Fenstermacher, K.D., Ginsburg, M.: Mining client-side activity for personalization. In: Fourth IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS 2002), pp. 205–212 (2002)Google Scholar

  • 31.

    Fu, Y., Creado, M., Ju, C.: Reorganizing web sites based on user access patterns. In: Proceedings of the tenth international conference on Information and knowledge management, pp. 583–585. ACM Press, New York (2001)Google Scholar

  • 32.

    Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)Google Scholar

  • 33.

    Hay, B., Wets, G., Vanhoof, K.: Clustering navigation patterns on a website using a sequence alignment methodGoogle Scholar

  • 34.

    Heer, J., Chi, H.: Mining the structure of user activity using cluster stability. In: Proceedings of the Workshop on Web Analytics, Second SIAM Conference on Data Mining, ACM Press, New York (2002)Google Scholar

  • 35.

    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975); Republished by the MIT press (1992) Google Scholar

  • 36.

    Huang, J.Z., Ng, M.K., Ching, W.-K., Ng, J., Cheung, D.: A cube model and cluster analysis for web access sessions. In: Kohavi, R., Masand, B., Spiliopoulou, M., Srivastava, J. (eds.) WebKDD 2001. LNCS, vol. 2356, pp. 48–67. Springer, Heidelberg (2002)CrossRefGoogle Scholar

  • 37.

    Huang, X., Cercone, N., An, A.: Comparison of interestingness functions for learning web usage patterns. In: Proceedings of the eleventh international conference on Information and knowledge management, pp. 617–620. ACM Press, New York (2002)CrossRefGoogle Scholar

  • 38.

    Joshi, K.P., Joshi, A., Yesha, Y.: On using a warehouse to analyze web logs. Distributed and Parallel Databases 13(2), 161–180 (2003)MATHCrossRefGoogle Scholar

  • 39.

    Kamdar, T.: Creating adaptive web servers using incremental web log mining. Master’s thesis, Computer Science Department, University of Maryland, Baltimore County (2001)Google Scholar

  • 40.

    Kosala, R., Blockeel, H.: Web mining research: A survey. In: SIGKDD: SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, vol. 2(1). ACM, New York (2000)Google Scholar

  • 41.

    Lan, B., Bressan, S., Ooi, B.C., Tan, K.-L.: Rule-assisted prefetching in web-server caching. In: Proceedings of the ninth international conference on Information and knowledge management (CIKM 2000), pp. 504–511. ACM Press, New York (2000)CrossRefGoogle Scholar

  • 42.

    Li, T.: Web-document prediction and presending using association rule sequential classifiers. Master’s thesis, Simon Fraser University (2001)Google Scholar

  • 43.

    Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalization based on association rule discovery from web usage data. In: Web Information and Data Management, pp. 9–15 (2001)Google Scholar

  • 44.

    Mortazavi-Asl, B.: Discovering and mining user web-page traversal patterns. Master’s thesis, Simon Fraser University (2001)Google Scholar

  • 45.

    Niu, N., Stroulia, E., El-Ramly, M.: Understanding web usage for dynamic web-site adaptation: A case study. In: Proceedings of the Fourth International Workshop on Web Site Evolution (WSE 2002), pp. 53–64. IEEE, Los Alamitos (2002)CrossRefGoogle Scholar

  • 46.

    Nanopoulos, A., Katsaros, D., Manolopoulos, Y.: Exploiting web log mining for web cache enhancement. In: Kohavi, R., Masand, B., Spiliopoulou, M., Srivastava, J. (eds.) WebKDD 2001. LNCS, vol. 2356, pp. 68–87. Springer, Heidelberg (2002)(Revised Papers)CrossRefGoogle Scholar

  • 47.

    Nanopoulos, A., Zakrzewicz, M., Morzy, T., Manolopoulos, Y.: Indexing web access-logs for pattern queries. In: Fourth ACM CIKM International Workshop on Web Information and Data Management, WIDM 2002 (2002)Google Scholar

  • 48.

    Nasraoui, O., Gonzalez, F., Dasgupta, D.: The fuzzy artificial immune system: Motivations, basic concepts, and application to clustering and web profiling. In: Proceedings of the World Congress on Computational Intelligence (WCCI) and IEEE International Conference on Fuzzy Systems, pp. 711–716 (2002)Google Scholar

  • 49.

    Oyanagi, S., Kubota, K., Nakase, A.: Application of matrix clustering to web log analysis and access prediction. In: WEBKDD 2001 - Mining Web Log Data Across All Customers Touch Points, Third International Workshop (2001)Google Scholar

  • 50.

    Paik, H.-Y., Benatallah, B., Hamadi, R.: Dynamic restructuring of e-catalog communities based on user interaction patterns. World Wide Web 5(4), 325–366 (2002)CrossRefGoogle Scholar

  • 51.

    Pei, J., Han, J., Mortazavi-asl, B., Zhu, H.: Mining access patterns efficiently from web logs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 396–407 (2000)Google Scholar

  • 52.

    Pilot Software. Web Site Analysis, Going Beyond Traffic Analysis (2002), http://www.marketwave.com/products_solutions/hitlist.html

  • 53.

    Punin, J.R., Krishnamoorthy, M.S., Zaki, M.J.: Logml: Log markup language for web usage mining. In: Kohavi, R., Masand, B., Spiliopoulou, M., Srivastava, J. (eds.) WEBKDD 2001. LNCS, vol. 2356, pp. 88–112. Springer, Heidelberg (2002)CrossRefGoogle Scholar

  • 54.

    Pedersen, T.B., Jespersen, S., Thorhauge, J.: A hybrid approach to web usage mining. Technical Report R02-5002, Department of Computer Science Aalborg University (2002)Google Scholar

  • 55.

    Ben Schafer, J., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Mining and Knowledge Discovery 5(1-2), 115–153 (2001)MATHCrossRefGoogle Scholar

  • 56.

    Shahabi, C., Banaei-Kashani, F.: A framework for efficient and anonymous web usage mining based on client-side tracking. In: Kohavi, R., Masand, B., Spiliopoulou, M., Srivastava, J. (eds.) WEBKDD 2001. LNCS, vol. 2356, pp. 113–144. Springer, Heidelberg (2002) (Revised Papers) CrossRefGoogle Scholar

  • 57.

    Shahabi, C., Chen, Y.-S.: Improving user profiles for e-commerce by genetic algorithms. E-Commerce and Intelligent Methods Studies in Fuzziness and Soft Computing 105(8) (2002)Google Scholar

  • 58.

    Srivastava, J., Cooley, R., Deshpande, M., Tan, P.-N.: Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 1(2), 12–23 (2000)CrossRefGoogle Scholar

  • 59.

    Stumme, G., Hotho, A., Berendt, B.: Usage mining for and on the semantic web. In: National Science Foundation Workshop on Next Generation Data Mining (2002)Google Scholar

  • 60.

    Tan, P.-N., Kumar, V.: Modeling of web robot navigational patterns. In: WEBKDD 2000 - Web Mining for E-Commerce – Challenges and Opportunities. Second International Workshop (August 2000)Google Scholar

  • 61.

    Tan, P.-N., Kumar, V.: Discovery of web robot sessions based on their navigational patterns. Data Mining and Knowledge Discovery 6(1), 9–35 (2002)CrossRefMathSciNetGoogle Scholar

  • 62.

    Toolan, F., Kushmerick, N.: Mining web logs for personalized site mapsGoogle Scholar

  • 63.

    VanderMeer, D., Dutta, K., Datta, A.: Enabling scalable online personalization on the web. In: Proceedings of the 2nd ACM E-Commerce Conference (EC 2000), pp. 185–196. ACM Press, New York (2000)CrossRefGoogle Scholar

  • 64.

    Wu, Y.-H., Chen, A.L.P.: Prediction of web page accesses by proxy server log. World Wide Web 5(1), 67–88 (2002)MATHCrossRefGoogle Scholar

  • 65.

    Xie, Y., Phoha, V.V.: Web user clustering from access log using belief function. In: Proceedings of the First International Conference on Knowledge Capture (K-CAP 2001), pp. 202–208. ACM Press, New York (2001)CrossRefGoogle Scholar

  • 66.

    Zaïane, O.R.: Web usage mining for a better web-based learning environment. In: Proceedings of Conference on Advanced Technology for Education, pp. 450–455 (2001)Google Scholar

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