Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient mining of association rules using closed itemset lattices
Information Systems
Data mining: concepts and techniques
Data mining: concepts and techniques
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
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
Why to Apply Generalized Disjunction-Free Generators Representation of Frequent Patterns?
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
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
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Closed Set Based Discovery of Representative Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Generating concise association rules
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Closures of Downward Closed Representations of Frequent Patterns
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Minimum description length principle: generators are preferable to closed patterns
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Using temporal constraints for temporal abstraction
Journal of Intelligent Information Systems
Compressed disjunction-free pattern representation versus essential pattern representation
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Reliable representations for association rules
Data & Knowledge Engineering
Essential patterns: a perfect cover of frequent patterns
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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For many data mining problems in order to solve them it is required to discover frequent patterns. Frequent itemsets are useful e.g. in the discovery of association and episode rules, sequential patterns and clusters. Nevertheless, the number of frequent itemsets is usually huge. Therefore, a number of lossless representations of frequent itemsets have recently been proposed. Two of such representations, namely the closed itemsets and the generators representation, are of particular interest as they can efficiently be applied for the discovery of most interesting non-redundant association and episode rules. On the other hand, it has been proved experimentally that other representations of frequent patterns happen to be more concise and more quickly extractable than these two representations even by several orders of magnitude. Hence, such concise representations seem to be an interesting alternative for materializing and reusing the knowledge of frequent patterns. The problem however arises, how to transform the intermediate representations into the desired ones efficiently and preferably without accessing the database. This article tackles this problem. As a result of investigating the properties of representations of frequent patterns, we offer a set of efficient algorithms for dataless transitioning between them.