Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
ICDE '97 Proceedings of the Thirteenth 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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Interpretations of Association Rules by Granular Computing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Optimized Disjunctive Association Rules via Sampling
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Design and application of hybrid intelligent systems
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Unsupervised Correlation Preserving Discretization
IEEE Transactions on Knowledge and Data Engineering
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
TAPER: A Two-Step Approach for All-Strong-Pairs Correlation Query in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Market basket analysis in a multiple store environment
Decision Support Systems
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Association mining in time-varying domains
Intelligent Data Analysis
A novel approach for discovering retail knowledge with price information from transaction databases
Expert Systems with Applications: An International Journal
Correlated pattern mining in quantitative databases
ACM Transactions on Database Systems (TODS)
An information-theoretic approach to quantitative association rule mining
Knowledge and Information Systems
Integrating in-process software defect prediction with association mining to discover defect pattern
Information and Software Technology
An association analysis approach to biclustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Evolutionary approach for mining association rules on dynamic databases
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Quantitative and ordinal association rules mining (QAR mining)
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Using data mining technique to enhance tax evasion detection performance
Expert Systems with Applications: An International Journal
Optimal leverage association rules with numerical interval conditions
Intelligent Data Analysis
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Mining association rules on large data sets has received considerable attention in recent years. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial, and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes. Optimized association rules are permitted to contain uninstantiated attributes and the problem is to determine instantiations such that either the support or confidence of the rule is maximized. In this paper, we generalize the optimized association rules problem in three ways: 1) association rules are allowed to contain disjunctions over uninstantiated attributes, 2) association rules are permitted to contain an arbitrary number of uninstantiated attributes, and 3) uninstantiated attributes can be either categorical or numeric. Our generalized association rules enable us to extract more useful information about seasonal and local patterns involving multiple attributes. We present effective techniques for pruning the search space when computing optimized association rules for both categorical and numeric attributes. Finally, we report the results of our experiments that indicate that our pruning algorithms are efficient for a large number of uninstantiated attributes, disjunctions, and values in the domain of the attributes.