Algorithms for clustering data
Algorithms for clustering data
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
ACM Computing Surveys (CSUR)
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Machine Learning and Its Applications, Advanced Lectures
Rule Evaluation Measures: A Unifying View
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring Interestingness Through Clustering: A Framework
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Using Information-Theoretic Measures to Assess Association Rule Interestingness
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Dilated chi-square: a novel interestingness measure to build accurate and compact decision list
Intelligent information processing II
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
CCCS: a top-down associative classifier for imbalanced class distribution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
New probabilistic interest measures for association rules
Intelligent Data Analysis
MINI: Mining Informative Non-redundant Itemsets
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Analyzing Behavior of Objective Rule Evaluation Indices Based on a Correlation Coefficient
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Loevinger's measures of rule quality for assessing cluster stability
Computational Statistics & Data Analysis
Uniform approximations for transcendental functions
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
Using background knowledge to rank itemsets
Data Mining and Knowledge Discovery
Re-examination of interestingness measures in pattern mining: a unified framework
Data Mining and Knowledge Discovery
A framework for mining interesting pattern sets
ACM SIGKDD Explorations Newsletter
Tell me what i need to know: succinctly summarizing data with itemsets
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Implication strength of classification rules
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Interesting Multi-relational Patterns
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
A data analysis approach for evaluating the behavior of interestingness measures
DS'05 Proceedings of the 8th international conference on Discovery Science
The amount of information that y gives about X
IEEE Transactions on Information Theory
Rare association rule mining via transaction clustering
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
A metric for unsupervised metalearning
Intelligent Data Analysis
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A number of studies, theoretical, empirical, or both, have been conducted to provide insight into the properties and behavior of interestingness measures for association rule mining. While each has value in its own right, most are either limited in scope or, more importantly, ignore the purpose for which interestingness measures are intended, namely the ultimate ranking of discovered association rules. This paper, therefore, focuses on an analysis of the rule-ranking behavior of 61 well-known interestingness measures tested on the rules generated from 110 different datasets. By clustering based on ranking behavior, we highlight, and formally prove, previously unreported equivalences among interestingness measures. We also show that there appear to be distinct clusters of interestingness measures, but that there remain differences among clusters, confirming that domain knowledge is essential to the selection of an appropriate interestingness measure for a particular task and business objective.