Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Scalable Techniques for Mining Causal Structures
Data Mining and Knowledge Discovery
Action-Rules: How to Increase Profit of a Company
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Postprocessing Decision Trees to Extract Actionable Knowledge
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Actionable Patterns by Role Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Extracting Actionable Knowledge from Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Mining action rules from scratch
Expert Systems with Applications: An International Journal
ARAS: action rules discovery based on agglomerative strategy
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Association Action Rules and Action Paths Triggered by Meta-actions
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
Action rules discovery system DEAR_3
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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In many business contexts, the ultimate goal of knowledge discovery is not the knowledge itself, but putting it to use. Models or patterns found by data mining methods often require further post-processing to bring this about. For instance, in churn prediction, data mining may give a model that predicts which customers are likely to end their contract, but companies are not just interested in knowing who is likely to do so, they want to know what they can do to avoid this. The models or patterns have to be transformed into actionable knowledge. Action mining explicitly addresses this. Currently, many action mining methods rely on a predictive model, obtained through data mining, to estimate the effect of certain actions and finally suggest actions with desirable effects. A major problem with this approach is that predictive models do not necessarily reflect a causal relationship between their inputs and outputs. This makes the existing action mining methods less reliable. In this paper, we introduce ICE-CREAM, a novel approach to action mining that explicitly relies on an automatically obtained best estimate of the causal relationships in the data. Experiments confirm that ICE-CREAM performs much better than the current state of the art in action mining.