Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Graphical models for discovering knowledge
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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Ranking the Interestingness of Summaries from Data Mining Systems
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Text Document Categorization by Term Association
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)
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Improving Associative Classification by Incorporating Novel Interestingness Measures
ICEBE '05 Proceedings of the IEEE International Conference on e-Business Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
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
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Post-mining of Association Rules: Techniques for Effective Knowledge Extraction
Post-mining of Association Rules: Techniques for Effective Knowledge Extraction
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
Frequent itemset mining of uncertain data streams using the damped window model
Proceedings of the 2011 ACM Symposium on Applied Computing
The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets
The Journal of Machine Learning Research
Mining classification rules without support: an anti-monotone property of Jaccard measure
DS'11 Proceedings of the 14th international conference on Discovery science
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Lattice based associative classifier
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Mining of multiobjective non-redundant association rules in data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
Confirmation measures of association rule interestingness
Knowledge-Based Systems
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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Associative classification is a rule-based approach to classify data relying on association rule mining by discovering associations between a set of features and a class label. Support and confidence are the de-facto "interestingness measures" used for discovering relevant association rules. The support-confidence framework has also been used in most, if not all, associative classifiers. Although support and confidence are appropriate measures for building a strong model in many cases, they are still not the ideal measures and other measures could be better suited. There are many other rule interestingness measures already used in machine learning, data mining and statistics. This work focuses on using 53 different objective measures for associative classification rules. A wide range of UCI datasets are used to study the impact of different "inter-estingness measures" on different phases of associative classifiers based on the number of rules generated and the accuracy obtained. The results show that there are interesting-ness measures that can significantly reduce the number of rules for almost all datasets while the accuracy of the model is hardly jeopardized or even improved. However, no single measure can be introduced as an obvious winner.