Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
ACM SIGKDD Explorations Newsletter
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
OntoEdit: Guiding Ontology Development by Methodology and Inferencing
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Analysis of navigation behaviour in web sites integrating multiple information systems
The VLDB Journal — The International Journal on Very Large Data Bases
The use of web structure and content to identify subjectively interesting web usage patterns
ACM Transactions on Internet Technology (TOIT)
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Data mining in course management systems: Moodle case study and tutorial
Computers & Education
A framework for mining meaningful usage patterns within a semantically enhanced web portal
Proceedings of the Third C* Conference on Computer Science and Software Engineering
Ontology-Based rummaging mechanisms for the interpretation of web usage patterns
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Semantic Formalization of Cross-Site User Browsing Behavior
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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Web Usage Mining (WUM) aims to extract navigation usage patterns from Web server logs. Mining algorithms yield usage patterns, but finding the ones that constitute new and interesting knowledge in the domain remains a challenge. Typically, analysts have to deal with a huge volume of pattern, from which they have to retrieve the potentially interesting one and interpret what they reveal about the domain. In this paper, we discuss the filtering mechanisms of O3R, an environment supporting the retrieval and interpretation of sequential navigation patterns. All O3R functionality is based on the availability of the domain ontology, which dynamically provides meaning to URLs. The analyst uses ontology concepts to define filters, which can be applied according to two filtering mechanisms: equivalence and similarity.