Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Principles of data mining
Empirical bayes screening for multi-item associations
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge refinement based on the discovery of unexpected patterns in data mining
Decision Support Systems - Special issue: Formal modeling and electronic commerce
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Significance Tests for Patterns in Continuous Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Detecting Interesting Instances
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Determining Hit Rate in Pattern Search
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Pattern discovery in sequences under a Markov assumption
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Modern Applied Statistics with S
Modern Applied Statistics with S
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
GAM: a guidance enabled association mining environment
International Journal of Business Intelligence and Data Mining
Statistical mining of interesting association rules
Statistics and Computing
A log-linear approach to mining significant graph-relational patterns
Data & Knowledge Engineering
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Pattern discovery has emerged as a direct result of increased data storage and analytic capabilities available to the data analyst. Without a massive amount of data, we do not have the evidence to support the discovery of the local deterministic structures that we call patterns. As such, pattern discovery is one of the few areas of data mining that cannot be considered simply as a 'scaling-up' of current statistical methodology to analyze large data sets. However, the philosophies of hypothesis testing and modeling in traditional statistics do lend themselves to forming a framework for pattern discovery, and we can also draw from ideas relating to outlier discovery and residual analysis to discover patterns. We illustrate an iterative strategy in a statistical framework by way of its application to one simulated and two real data sets.