Word association norms, mutual information, and lexicography
Computational Linguistics
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 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
A new framework for itemset generation
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-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
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Data Mining Using Grammar-Based Genetic Programming and Applications
Data Mining Using Grammar-Based Genetic Programming and Applications
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Efficient Mining of High Confidience Association Rules without Support Thresholds
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Clinical Knowledge Discovery in Hospital Information Systems: Two Case Studies
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Applying Objective Interestingness Measures in Data Mining Systems
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Free Itemsets under Constraints
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
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
Rule Evaluation Measures: A Unifying View
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
Data Mining and Knowledge Discovery
Mining risk patterns in medical data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
IEEE Transactions on Knowledge and Data Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Top 10 algorithms in data mining
Knowledge and Information Systems
A Unified View of Objective Interestingness Measures
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Discovering Knowledge from Local Patterns with Global Constraints
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
A new method for ranking discovered rules from data mining by DEA
Expert Systems with Applications: An International Journal
On Optimal Rule Mining: A Framework and a Necessary and Sufficient Condition of Antimonotonicity
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Measures of ruleset quality for general rules extraction methods
International Journal of Approximate Reasoning
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
Mining non-coincidental rules without a user defined support threshold
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Optimized rule mining through a unified framework for interestingness measures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Pushing tougher constraints in frequent pattern mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Aggregation of valued relations applied to association rule interestingness measures
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Confirmation measures of association rule interestingness
Knowledge-Based Systems
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Many studies have shown the limits of the support/confidence framework used in Apriori -like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support, but candidate set generation (e.g., frequent item set mining) is still costly. In addition, many rules are uninteresting or redundant and one can miss interesting rules like nuggets. We are thus facing a complexity issue and a quality issue. One solution is to not use frequent itemset mining and to focus as soon as possible on interesting rules using additional interestingness measures. We present here a formal framework that allows us to make a link between analytic and algorithmic properties of interestingness measures. We introduce the notion of optimonotony in relation with the optimal rule discovery framework. We then demonstrate a necessary and sufficient condition for the existence of optimonotony. This result can thus be applied to classify the measures. We study the case of 39 classical measures and show that 31 of them are optimonotone. These optimonotone measures can thus be used with an underlying pruning strategy. Empirical evaluations show that the pruning strategy is efficient and leads to the discovery of nuggets using an optimonotone measure and without the support constraint. © 2012 Wiley Periodicals, Inc.