Data Mining of User Navigation Patterns
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
Adaptive on-line page importance computation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Knowledge discovery from users Web-page navigation
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Page quality: in search of an unbiased web ranking
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Web Structure Mining for Usability Analysis
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Discrimination of personal web pages by extracting subjective expressions
International Journal of Business Intelligence and Data Mining
Identifying Social Communities by Frequent Pattern Mining
IV '09 Proceedings of the 2009 13th International Conference Information Visualisation
Category mapping for the automatic integration of category-constrained web search
International Journal of Business Intelligence and Data Mining
Discovery of Interesting Association Rules Based on Web Usage Mining
MEDIACOM '10 Proceedings of the 2010 International Conference on Multimedia Communications
WebUser: mining unexpected web usage
International Journal of Business Intelligence and Data Mining
From alternative clustering to robust clustering and its application to gene expression data
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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The work described in this paper is motivated by the fact that the structure of a website may not satisfy a larger population of the visiting users who may jump between pages of the website before they land on the target page(s); this is at least partially true because access patterns were not known when the website was designed. We developed a robust framework that tackles this problem by considering both web log data and web structure data to suggest a more compact structure that could satisfy a larger user group. The study assumes the trend recorded so far in the web log reflects well the anticipated behaviour of the users in the future. We separately analyse web log and web structure data using three techniques, namely clustering, frequent pattern mining and network analysis. The final outcome from the two stages is reflected on to one of the six models, namely the network of pages to report linking pages by the most appropriate connections.