Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Designing Templates for Mining Association Rules
Journal of Intelligent Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Constructing Approximate Informative Basis of Association Rules
DS '01 Proceedings of the 4th International Conference on Discovery Science
Distributed and Shared Memory Algorithm for Parallel Mining of Association Rules
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Redundant association rules reduction techniques
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A novel approach of multilevel positive and negative association rule mining for spatial databases
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Discovering temporal bisociations for linking concepts over time
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Time-based discovery in biomedical literature: mining temporal links
International Journal of Data Analysis Techniques and Strategies
A prediction framework based on contextual data to support Mobile Personalized Marketing
Decision Support Systems
Expert Systems with Applications: An International Journal
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Association rules (AR) are a class of patterns which describe regularities in a set of transactions. When items of transactions are organized in a taxonomy, AR can be associated with a level of the taxonomy since they contain only items at that level. A drawback of multiple level AR mining is represented by the generation of redundant rules which do not add further information to that expressed by other rules. In this paper, a method for the discovery of non-redundant multiple level AR is proposed. It follows the usual two-stepped procedure for AR mining and it prunes redundancies in each step. In the first step, redundancies are removed by resorting to the notion of multiple level closed frequent itemsets , while in the second step, pruning is based on an extension of the notion of minimal rules . The proposed technique has been applied to a real case of analysis of textual data. An empirical comparison with the Apriori algorithm proves the advantages of the proposed method in terms of both time-performance and redundancy reduction.