Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for 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
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
Size-estimation framework with applications to transitive closure and reachability
Journal of Computer and System Sciences
Mining optimized gain rules for numeric attributes
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Introduction to Algorithms
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
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
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Mutually Dependent Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Integrating Classification and Association Rule Mining: A Concept Lattice Framework
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on 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
Quantitative Association Rules Mining Methods with Privacy-preserving
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
TAPER: A Two-Step Approach for All-Strong-Pairs Correlation Query in Large Databases
IEEE Transactions on Knowledge and Data Engineering
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
Data Mining and Knowledge Discovery
Finding highly correlated pairs efficiently with powerful pruning
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Correlation search in graph databases
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An information-theoretic approach to quantitative association rule mining
Knowledge and Information Systems
Using classification to evaluate the output of confidence-based association rule mining
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Event Correlations in Sensor Networks
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Electronic Commerce Research and Applications
GENCCS: a correlated group difference approach to contrast set mining
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Developing RFID database models for analysing moving tags in supply chain management
ER'11 Proceedings of the 30th international conference on Conceptual modeling
CGStream: continuous correlated graph query for data streams
Proceedings of the 21st ACM international conference on Information and knowledge management
Continuous top-k query for graph streams
Proceedings of the 21st ACM international conference on Information and knowledge management
A bayesian approach for classification rule mining in quantitative databases
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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We study mining correlations from quantitative databases and show that this is a more effective approach than mining associations to discover useful patterns. We propose the novel notion of quantitative correlated pattern (QCP), which is founded on two formal concepts, mutual information and all-confidence. We first devise a normalization on mutual information and apply it to the problem of QCP mining to capture the dependency between the attributes. We further adopt all-confidence as a quality measure to ensure, at a finer granularity, the dependency between the attributes with specific quantitative intervals. We also propose an effective supervised method that combines the consecutive intervals of the quantitative attributes based on mutual information, such that the interval-combining is guided by the dependency between the attributes. We develop an algorithm, QCoMine, to mine QCPs efficiently by utilizing normalized mutual information and all-confidence to perform bilevel pruning. We also identify the redundancy existing in the set of QCPs and propose effective techniques to eliminate the redundancy. Our extensive experiments on both real and synthetic datasets verify the efficiency of QCoMine and the quality of the QCPs. The experimental results also justify the effectiveness of our proposed techniques for redundancy elimination. To further demonstrate the usefulness and the quality of QCPs, we study an application of QCPs to classification. We demonstrate that the classifier built on the QCPs achieves higher classification accuracy than the state-of-the-art classifiers built on association rules.