Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
MINI: Mining Informative Non-redundant Itemsets
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Minimum-Size Bases of Association Rules
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Closures of Downward Closed Representations of Frequent Patterns
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Data & Knowledge Engineering
A new generic basis of "factual" and "implicative" association rules
Intelligent Data Analysis
Closed Non Derivable Data Cubes Based on Non Derivable Minimal Generators
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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
Block interaction: a generative summarization scheme for frequent patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Frequent regular itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Reliable representations for association rules
Data & Knowledge Engineering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A Framework for Synthesizing Arbitrary Boolean Queries Induced by Frequent Itemsets
International Journal of Knowledge-Based Organizations
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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Itemset mining typically results in large amounts of redundant itemsets. Several approaches such as closed itemsets, non-derivable itemsets and generators have been suggested for losslessly reducing the amount of itemsets. We propose a new pruning method based on combining techniques for closed and non-derivable itemsets that allows further reductions of itemsets. This reduction is done without loss of information, that is, the complete collection of frequent itemsets can still be derived from the collection of closed non-derivable itemsets. The number of closed non-derivable itemsets is bound both by the number of closed and the number of non-derivable itemsets, and never exceeds the smaller of these. Our experiments show that the reduction is significant in some datasets.