Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
On the Effectiveness of Web Usage Mining for Page Recommendation and Restructuring
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
Building a Recommender Agent for e-Learning Systems
ICCE '02 Proceedings of the International Conference on Computers in Education
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
Data mining for web personalization
The adaptive web
Social navigation support in a course recommendation system
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Using rules discovery for the continuous improvement of e-learning courses
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Data mining in course management systems: Moodle case study and tutorial
Computers & Education
Data mining for adaptive learning sequence in English language instruction
Expert Systems with Applications: An International Journal
MAGADI: a Blended-Learning Framework for Overall Learning
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
The influence of adaptation on hypertext structures and navigation
Proceedings of the 21st ACM conference on Hypertext and hypermedia
An approach to design the student interaction based on the recommendation of e-learning objects
Proceedings of the 28th ACM International Conference on Design of Communication
Building group recommendations in e-learning systems
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Data mining for adaptive learning in a TESL-based e-learning system
Expert Systems with Applications: An International Journal
Introducing affective agents in recommendation systems based on relational data clustering
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
An efficient web recommendation system based on modified IncSpan algorithm
International Journal of Knowledge and Web Intelligence
Designing a user interface to manage recommendations in virtual learning communities
International Journal of Web Based Communities
Building group recommendations in e-learning systems
Transactions on Computational Collective Intelligence VII
Semi-automatic assembly of learning resources
Computers & Education
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In this paper, we describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable educational hypermedia system. We present a specific mining tool and a recommender engine that we have integrated in the AHA! system in order to help the teacher to carry out the whole web mining process. We report on several experiments with real data in order to show the suitability of using both clustering and sequential pattern mining algorithms together for discovering personalized recommendation links.