Guest Editorial: Global modeling using local patterns
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An enhanced relevance criterion for more concise supervised pattern discovery
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Siren: an interactive tool for mining and visualizing geospatial redescriptions
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Top-N minimization approach for indicative correlation change mining
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Subgroup discovery using bump hunting on multi-relational histograms
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Predictive sequence miner in ILP learning
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Contrast mining from interesting subgroups
Bisociative Knowledge Discovery
A pattern mining based integrative framework for biomarker discovery
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Explaining subgroups through ontologies
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A bayesian scoring technique for mining predictive and non-spurious rules
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Generic pattern trees for exhaustive exceptional model mining
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Context mining and integration into predictive web analytics
Proceedings of the 22nd international conference on World Wide Web companion
A study of subgroup discovery approaches for defect prediction
Information and Software Technology
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
MiningZinc: a modeling language for constraint-based mining
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper gives a survey of contrast set mining (CSM), emerging pattern mining (EPM), and subgroup discovery (SD) in a unifying framework named supervised descriptive rule discovery. While all these research areas aim at discovering patterns in the form of rules induced from labeled data, they use different terminology and task definitions, claim to have different goals, claim to use different rule learning heuristics, and use different means for selecting subsets of induced patterns. This paper contributes a novel understanding of these subareas of data mining by presenting a unified terminology, by explaining the apparent differences between the learning tasks as variants of a unique supervised descriptive rule discovery task and by exploring the apparent differences between the approaches. It also shows that various rule learning heuristics used in CSM, EPM and SD algorithms all aim at optimizing a trade off between rule coverage and precision. The commonalities (and differences) between the approaches are showcased on a selection of best known variants of CSM, EPM and SD algorithms. The paper also provides a critical survey of existing supervised descriptive rule discovery visualization methods.