C4.5: programs for machine learning
C4.5: programs for machine learning
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
User profiling in personalization applications through rule discovery and validation
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing association rules for feedback within the legal system
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Opportunity map: a visualization framework for fast identification of actionable knowledge
Proceedings of the 14th ACM international conference on Information and knowledge management
A Visual Data Mining Framework for Convenient Identification of Useful Knowledge
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Assessing data mining results via swap randomization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
A Profit-Based Business Model for Evaluating Rule Interestingness
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Mining Non-redundant Reclassification Rules
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining action rules from scratch
Expert Systems with Applications: An International Journal
Explanation oriented association mining using rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Combined association rule mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Study of positive and negative association rules based on multi-confidence and chi-squared test
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
A distance-based approach for action recommendation
ECML'05 Proceedings of the 16th European conference on Machine Learning
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
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Building predictive models and finding useful rules are two important tasks of data mining. While building predictive models has been well studied, finding useful rules for action still presents a major problem. A main obstacle is that many data mining algorithms often produce too many rules. Existing research has shown that most of the discovered rules are actually redundant or insignificant. Pruning techniques have been developed to remove those spurious and/or insignificant rules. In this paper, we argue that being a significant rule (or a non-redundant rule), however, does not mean that it is a potentially useful rule for action. Many significant rules (unpruned rules) are in fact not actionable. This paper studies this issue and presents an efficient algorithm to identify these non-actionable rules. Experiment results on many real-life datasets show that the number of non-actionable rules is typically quite large. The proposed technique thus enables the user to focus on fewer rules and to be assured that the remaining rules are non-redundant and potentially useful for action.