Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Efficient Mining of High Confidience Association Rules without Support Thresholds
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Confident Minimal Rules with Fixed-Consequents
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Finding sporadic rules in the diagnosis of the Erythemato-Squamous diseases
Intelligent Data Analysis
Finding minimal rare itemsets and rare association rules
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
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Detecting association rules with low support but high confidence is a difficult data mining problem. To find such rules using approaches like the Apriori algorithm, minimum support must be set very low, which results in a large amount of redundant rules. We are interested in sporadic rules; i.e. those that fall below a maximum support level but above the level of support expected from random coincidence. In this paper we introduce an algorithm called MIISR to find a particular type of sporadic rule efficiently: where the support of the antecedent as a whole falls below maximum support, but where items may have quite high support individually. Our proposed method uses item constraints and coincidence pruning to discover these rules in reasonable time.