Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
C4.5: programs for machine learning
C4.5: programs for machine learning
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
Fast discovery of association rules
Advances in 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
Unexpectedness as a measure of interestingness in knowledge discovery
Decision Support Systems - Special issue on WITS '97
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Machine Learning
A General Measure of Rule Interestingness
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Twain: Two-end association miner with precise frequent exhibition periods
ACM Transactions on Knowledge Discovery from Data (TKDD)
Interactive visual exploration of association rules with rule-focusing methodology
Knowledge and Information Systems
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Finding Functional Groups of Objective Rule Evaluation Indices Using PCA
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
Finding the Most Interesting Association Rules by Aggregating Objective Interestingness Measures
Knowledge Acquisition: Approaches, Algorithms and Applications
A Comparison of Composed Objective Rule Evaluation Indices Using PCA and Single Indices
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Improving a rule evaluation support method based on objective indices
International Journal of Advanced Intelligence Paradigms
Analyzing correlation coefficients of objective rule evaluation indices on classification rules
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Scaling up top-K cosine similarity search
Data & Knowledge Engineering
Interestingness measures for association rules based on statistical validity
Knowledge-Based Systems
Building a topic hierarchy using the bag-of-related-words representation
Proceedings of the 11th ACM symposium on Document engineering
Cosine interesting pattern discovery
Information Sciences: an International Journal
Implication strength of classification rules
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Efficient Search Methods for Statistical Dependency Rules
Fundamenta Informaticae - Machine Learning in Bioinformatics
Scaling up cosine interesting pattern discovery: A depth-first method
Information Sciences: an International Journal
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, there exists no information-theoretic measure which is adapted to the semantics of association rules. In this article, we present the Directed Information Ratio (DIR), a new rule interestingness measure which is based on information theory. DIR is specially designed for association rules, and in particular it differentiates two opposite rules a → b and a → \mathop b\limits^ - . Moreover, to our knowledge, DIR is the only rule interestingness measure which rejects both independence and (what we call) equilibrium, i.e. it discards both the rules whose antecedent and consequent are negatively correlated, and the rules which have more counter-examples than examples. Experimental studies show that DIR is a very filtering measure, which is useful for association rule post-processing.