Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on 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
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Finding sporadic rules in the diagnosis of the Erythemato-Squamous diseases
Intelligent Data Analysis
Mining significant least association rules using fast SLP-growth algorithm
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
Finding minimal rare itemsets and rare association rules
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Scalable model for mining critical least association rules
ICICA'10 Proceedings of the First international conference on Information computing and applications
RM-Tool: A framework for discovering and evaluating association rules
Advances in Engineering Software
RP-Tree: rare pattern tree mining
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Mining interesting imperfectly sporadic rules
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Outlier detection in relational data: A case study in geographical information systems
Expert Systems with Applications: An International Journal
Efficient mining of dissociation rules
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Valency based weighted association rule mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Detecting stealthy backdoors with association rule mining
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part II
Weighted association rule mining via a graph based connectivity model
Information Sciences: an International Journal
Rare pattern mining on data streams
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Automatic Item Weight Generation for Pattern Mining and its Application
International Journal of Data Warehousing and Mining
Tracing significant association rules using critical least association rules model
International Journal of Innovative Computing and Applications
Rare association rule mining via transaction clustering
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Exploring Sequential and Association Rule Mining for Pattern-based Energy Demand Characterization
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets
Data Mining and Knowledge Discovery
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
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We define sporadic rules as those with low support but high confidence: for example, a rare association of two symptoms indicating a rare disease. To find such rules using the well-known Apriori algorithm, minimum support has to be set very low, producing a large number of trivial frequent itemsets. We propose “Apriori-Inverse”, a method of discovering sporadic rules by ignoring all candidate itemsets above a maximum support threshold. We define two classes of sporadic rule: perfectly sporadic rules (those that consist only of items falling below maximum support) and imperfectly sporadic rules (those that may contain items over the maximum support threshold). We show that Apriori-Inverse finds all perfectly sporadic rules much more quickly than Apriori. We also propose extensions to Apriori-Inverse to allow us to find some (but not necessarily all) imperfectly sporadic rules.