Warehousing and mining Web logs
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Separating the swarm: categorization methods for user sessions on the web
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Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
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Prediction of Web Page Accesses by Proxy Server Log
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Web mining for web personalization
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Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
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WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
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WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
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DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
Analysis of navigation behaviour in web sites integrating multiple information systems
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Web usage mining: discovery and applications of usage patterns from Web data
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Privacy conflicts in CRM services for online shops: a case study
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
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Advanced Engineering Informatics
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As a confluence of data mining and WWW technologies, it is now possible to perform data mining on web log records collected from the Internet web page access history. The behaviour of the web page readers is imprinted in the web server log files. Analyzing and exploring regularities in this behaviour can improve system performance, enhance the quality and delivery of Internet information services to the end user, and identify population of potential customers for electronic commerce. Thus, by observing people using collections of data, data mining can bring considerable contribution to digital library designers. In a joint effort between the TeleLearning-NCE project on Virtual University and NCE-IRIS project on data mining, we have been developing the knowledge discovery tool, WebLogMiner, for mining web server log files. This paper presents the design of the WebLogMiner, reports the current progress, and outlines the future work in this direction.