Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
A condensed representation to find frequent patterns
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
Reducing borders of k-disjunction free representations of frequent patterns
Proceedings of the 2004 ACM symposium on Applied computing
Data Mining and Knowledge Discovery
Discovering Synonyms Based on Frequent Termsets
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Word Sense Discovery for Web Information Retrieval
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Discovering word meanings based on frequent termsets
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Essential patterns: a perfect cover of frequent patterns
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Generalization of association rules through disjunction
Annals of Mathematics and Artificial Intelligence
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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The discovery of frequent patterns is one of the most important issues in the data mining area. A major difficulty concerning frequent patterns is huge amount of discovered patterns. The problem can be significantly alleviated by applying concise representations of frequent patterns. In this paper, we offer new lossless representations of frequent patterns that are derivable from downward closed representations by replacing the original elements and eventually some border ones with their closures. We show for which type of downward closed representations the additional closures are superfluous and for which they need to be stored. If the additional closures are not stored, the new representations are guaranteed to be not less concise than the original ones.