Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Database Systems (TODS)
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
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
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Pruning derivative partial rules during impact rule discovery
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
On Optimal Rule Mining: A Framework and a Necessary and Sufficient Condition of Antimonotonicity
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining top-k regular-frequent itemsets using database partitioning and support estimation
Expert Systems with Applications: An International Journal
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
Learning theory analysis for association rules and sequential event prediction
The Journal of Machine Learning Research
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Traditional association rulemining techniques employ the support and confidence framework. However, specifying minimum support of the mined rules in advance often leads to either too many or too few rules, which negatively impacts the performance of the overall system. Here we propose replacing Apriori's user-defined minimum support threshold with the more meaningful MinAbsSup function. This calculates a custom minimum support for each itemset based on the probability of chance collision of its items, as derived from the inverse of Fisher's exact test. We will introduce the notion of coincidental itemsets; given a transaction dataset there is a chance that two independent items are appearing together by random coincidence. Rules generated from these itemsets do not denote a meaningful association, and are not useful.