Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Discovery of Interesting Association Rules from Livelink Web Log Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Meta-learning for post-processing of association rules
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Integer linear programming models for constrained clustering
DS'10 Proceedings of the 13th international conference on Discovery science
Simple and effective behavior tracking by post processing of association rules into segments
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
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We propose two algorithms for grouping and summarizingassociation rules. The first algorithm recursively groupsrules according to the structure of the rules and generatesa tree of clusters as a result. The second algorithm groupsthe rules according to the semantic distance between therules by making use of an autometically tagged semantictree-structured network of items. We provide a case study inwhich the proposed algorithms are evaluated. The resultsshow that our grouping methods are effective and producegood grouping results.