Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
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WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
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
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
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Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
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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)
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Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
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Experiences in building a tool for navigating association rule result sets
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
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Information Systems - Knowledge discovery and data mining (KDD 2002)
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Computers & Education
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ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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Web Semantics: Science, Services and Agents on the World Wide Web
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
Clustering web sessions by levels of page similarity
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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
Ontology-based filtering mechanisms for web usage patterns retrieval
EC-Web'05 Proceedings of the 6th international conference on E-Commerce and Web Technologies
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
An ontology-based universal design knowledge support system
Knowledge-Based Systems
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Knowledge-Based Systems
Finding association rules in semantic web data
Knowledge-Based Systems
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Web Usage Mining (WUM) is the application of data mining techniques over Web server logs in order to extract navigation usage patterns. The analysis of mining patterns for assessing the knowledge they reveal is a critical phase in WUM. The main challenges are: (a) mining algorithms yield a huge number of patterns and (b) there is a significant semantic gap between URLs and events performed by users. In this paper, we describe the pattern analysis mechanisms integrated in O3R (Ontology-based Rules Retrieval and Rummaging), a human-centered environment for the analysis of navigation rules. O3R explores the synergy of mechanisms for retrieving and analyzing patterns. Filtering and clustering allow users to retrieve subsets of patterns with specific characteristics, in order to deal with the large volume of patterns. Rummaging mechanisms are targeted at assessing the meaning and relevance of pattern with regard to the domain, and it is particularly suitable for exploratory analysis. The distinctive feature of O3R is that is dynamically associates meaning to patterns using the concepts and relationships of a domain ontology, as a means of reducing the gap between syntactic URLs and semantic events performed by users. The paper describes the mechanisms in detail, and explores their synergic integration in the O3R prototype. It also reports two case studies that evaluate the use of O3R for the analysis of navigation patterns of a learning site.