Elements of information theory
Elements of information theory
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Data Mining with optimized two-dimensional association rules
ACM Transactions on Database Systems (TODS)
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An evolutionary algorithm to discover numeric association rules
Proceedings of the 2002 ACM symposium on Applied computing
Multipass algorithms for mining association rules in text databases
Knowledge and Information Systems
Introduction to Algorithms
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Optimized Gain Rules for Numeric Attributes
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
Discovering Numeric Association Rules via Evolutionary Algorithm
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Quantitative Association Rules Based on Half-Spaces: An Optimization Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Quantitative Association Rules Mining Methods with Privacy-preserving
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Multiple labels associative classification
Knowledge and Information Systems
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
An Algorithm for Privacy-Preserving Quantitative Association Rules Mining
DASC '06 Proceedings of the 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing
QuantMiner: a genetic algorithm for mining quantitative association rules
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Correlated pattern mining in quantitative databases
ACM Transactions on Database Systems (TODS)
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
Mining dynamic association rules with comments
Knowledge and Information Systems
Mining fuzzy association rules from uncertain data
Knowledge and Information Systems
Mining frequent patterns from univariate uncertain data
Data & Knowledge Engineering
Mining numerical association rules via multi-objective genetic algorithms
Information Sciences: an International Journal
Optimal leverage association rules with numerical interval conditions
Intelligent Data Analysis
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Quantitative association rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike boolean association rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. However, the combination of these quantitative attributes and their value intervals always gives rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency. In this paper, we propose an information-theoretic approach to avoid unrewarding combinations of both the attributes and their value intervals being generated in the mining process. We study the mutual information between the attributes in a quantitative database and devise a normalization on the mutual information to make it applicable in the context of QAR mining. To indicate the strong informative relationships among the attributes, we construct a mutual information graph (MI graph), whose edges are attribute pairs that have normalized mutual information no less than a predefined information threshold. We find that the cliques in the MI graph represent a majority of the frequent itemsets. We also show that frequent itemsets that do not form a clique in the MI graph are those whose attributes are not informatively correlated to each other. By utilizing the cliques in the MI graph, we devise an efficient algorithm that significantly reduces the number of value intervals of the attribute sets to be joined during the mining process. Extensive experiments show that our algorithm speeds up the mining process by up to two orders of magnitude. Most importantly, we are able to obtain most of the high-confidence QARs, whereas the QARs that are not returned by MIC are shown to be less interesting.