Expert systems techniques, tools and applications
Expert systems techniques, tools and applications
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Pattern-Directed Inference Systems
Pattern-Directed Inference Systems
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
An iterative hypothesis-testing strategy for pattern discovery
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical mining of interesting association rules
Statistics and Computing
An efficient rigorous approach for identifying statistically significant frequent itemsets
Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Local pattern detection and clustering
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Features for learning local patterns in time-stamped data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets
Journal of the ACM (JACM)
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The problem of spurious apparent patterns arising by chance is a fundamental one for pattern detection. Classical approaches, based on adjustments such as the Bonferroni procedure, are arguably not appropriate in a data mining context. Instead, methods based on the false discovery rate - the proportion of flagged patterns which do not represent an underlying reality - may be more relevant. We describe such procedures and illustrate their application on a marketing dataset.