Evidential support logic programming
Fuzzy Sets and Systems
Concept learning from examples and counter examples
International Journal of Man-Machine Studies
Silk from a sow's ear: extracting usable structures from the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
SiteHelper: a localized agent that helps incremental exploration of the World Wide Web
Selected papers from the sixth international conference on World Wide Web
Adaptive Web sites: automatically synthesizing Web pages
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Summary of WWW characterizations
WWW7 Proceedings of the seventh international conference on World Wide Web 7
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs
ADL '98 Proceedings of the Advances in Digital Libraries Conference
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
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Mining Indirect Associations in Web Data
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Detail and Context in Web Usage Mining: Coarsening and Visualizing Sequences
WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Extended Real-Time Learning Behavior Mining
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Independent component analysis and rough fuzzy based approach to web usage mining
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Mining usage web log via independent component analysis and rough fuzzy
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
APD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Finding unexpected navigation behaviour in clickstream data for website design improvement
Journal of Web Engineering
Integrating web conceptual modeling and web usage mining
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Discovering better navigation sequences for the session construction problem
Data & Knowledge Engineering
Employing inductive databases in concrete applications
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A website mining model centered on user queries
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Guest editorial: special issue on a decade of mining the Web
Data Mining and Knowledge Discovery
A crowdsourced approach for concern-sensitive integration of information across the web
Journal of Web Engineering
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Web Usage Mining is the application of data mining techniques to large Web data repositories in order to extract usage patterns. As with many data mining application domains, the identification of patterns that are considered interesting is a problem that must be solved in addition to simply generating them. Aneces sary step in identifying interesting results is quantifying what is considered uninteresting in order to form a basis for comparison. Several research efforts have relied on manually generated sets of uninteresting rules. However, manual generation of a comprehensive set of evidence about beliefs for a particular domain is impractical in many cases. Generally, domain knowledge can be used to automatically create evidence for or against a set of beliefs. This paper develops a quantitative model based on support logic for determining the interestingness of discovered patterns. For Web Usage Mining, there are three types of domain information available; usage, content, and structure. This paper also describes algorithms for using these three types of information to automatically identify interesting knowledge. These algorithms have been incorporated into the Web Site Information Filter (WebSIFT) system and examples of interesting frequent itemsets automatically discovered from real Web data are presented.