Efficient mining of association rules using closed itemset lattices
Information Systems
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 Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Finding Top-N Pseudo Formal Concepts with Core Intents
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
An extended branch and bound search algorithm for finding top-N formal concepts of documents
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
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In the study of discovering association rules, it is regarded as an important task to reduce the number of generated rules without loss of any information about the significant rules. From this point of view, Bastide, et al. have proposed to generate only non-redundant rules [2]. Although the number of generated rules can be reduced drastically by taking the redundancy into account, many rules are often still generated. In this paper, we try to propose a method for reducing the number of the generated rules by extending the original framework. For this purpose, we introduce a notion of approximate generator and consider an approximate redundancy. According to our new notion of redundancy, many non-redundant rules in the original sense are judged redundant and invisible to users. This achieves the reduction of generated rules. Furthermore, it is shown that any redundant rule can be easily reconstructed from our non-redundant rule with its approximate support and confidence. The maximum errors of these values can be evaluated by a user-defined parameter. We present an algorithm for constructing a set of non-redundant rules, called an approximate informative basis. The completeness and weak-soundness of the basis are theoretically shown. Any significant rule can be reconstructed from the basis and any rule reconstructed from the basis is (approximately) significant. Some experimental results show an effectiveness of our method as well.