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 parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining frequent patterns with counting inference
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Mining interesting imperfectly sporadic rules
Knowledge and Information Systems
A Fast Algorithm for Mining Rare Itemsets
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
RP-Tree: rare pattern tree mining
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining frequent itemsets in data streams within a time horizon
Data & Knowledge Engineering
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In this paper we face the problem of searching for rare itemsets. A main issue regards the strategy to adopt in exploring the power set lattice. Assuming a power set lattice with full set at the top and empty set at the bottom, the most of the algorithms adopt a bottom-up exploration, i.e. moving from smaller to larger sets. Although this approach is advantageous in the case of frequent itemsets, it might not be worth being used for rare itemsets, as they occur on the top of the lattice. We propose Rarity, a top-down breadth-first level-wise algorithm. Experimental results and comparisons are illustrated in order to provide a quantitative characterization of algorithm performances and complexity. Application to some UCI benchmark and real world datasets is provided. An algorithm parallelization is outlined. Experiments showed that this approach takes advantage of finding all rare non-zero itemsets in less time than other solutions, at expenses of higher memory demand.