Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An incremental concept formation approach for learning from databases
Theoretical Computer Science - Special issue on formal methods in databases and software engineering
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Representative Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Interpolation techniques for geo-spatial association rule mining
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Interpolation Models for Spatiotemporal Association Mining
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
Semi-supervised learning on closed set lattices
Intelligent Data Analysis
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A set of association rules is called representative if it is a minimal set of rules from which all association rules can be generated. The existing algorithms for generating representative association rules use all the frequent itemsets as input. In this paper, we present a new approach for generating representative association rules that uses only a subset of the set of frequent itemsets called frequent closed itemsets. This results in a big reduction in the input size and, therefore, faster algorithms for generating representative association rules. Our approach uses ideas from formal concept analysis to find frequent closed itemsets.