Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical bayes screening for multi-item associations
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding the most interesting patterns in a database quickly by using sequential sampling
The Journal of Machine Learning Research
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth 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
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
Assessing data mining results via swap randomization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding association rules that trade support optimally against confidence
Intelligent Data Analysis
Discovering Significant Patterns
Machine Learning
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
Why is rule learning optimistic and how to correct it
ECML'06 Proceedings of the 17th European conference on Machine Learning
Tell me something I don't know: randomization strategies for iterative data mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Ensembles of jittered association rule classifiers
Data Mining and Knowledge Discovery
Intelligent Data Analysis
Human disease network guided discovery of interesting itemsets in hospital discharge data
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
Multiple hypothesis testing in pattern discovery
DS'11 Proceedings of the 14th international conference on Discovery science
Controlling false positives in association rule mining
Proceedings of the VLDB Endowment
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Discovering associations in high-dimensional data
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
From Association Analysis to Causal Discovery
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
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Standard pattern discovery techniques, such as association rules, suffer an extreme risk of finding very large numbers of spurious patterns for many knowledge discovery tasks. The direct-adjustment approach to controlling this risk applies a statistical test during the discovery process, using a critical value adjusted to take account of the size of the search space. However, a problem with the direct-adjustment strategy is that it may discard numerous true patterns. This paper investigates the assignment of different critical values to different areas of the search space as an approach to alleviating this problem, using a variant of a technique originally developed for other purposes. This approach is shown to be effective at increasing the number of discoveries while still maintaining strict control over the risk of false discoveries.