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
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
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
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
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
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Correlation search in graph databases
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mining non-redundant high order correlations in binary data
Proceedings of the VLDB Endowment
An Improved Algorithm for Mining Non-Redundant Interacting Feature Subsets
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Efficient algorithms for mining constrained frequent patterns from uncertain data
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
MACs: Multi-Attribute Co-clusters with High Correlation Information
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A Fast Feature-Based Method to Detect Unusual Patterns in Multidimensional Datasets
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
Effective, design-independent XML keyword search
Proceedings of the 18th ACM conference on Information and knowledge management
Keyword search for data-centric XML collections with long text fields
Proceedings of the 13th International Conference on Extending Database Technology
Tight correlated item sets and their efficient discovery
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
An algorithm to mine general association rules from tabular data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Efficient algorithms for the mining of constrained frequent patterns from uncertain data
ACM SIGKDD Explorations Newsletter
Using structural information in XML keyword search effectively
ACM Transactions on Database Systems (TODS)
Contrasting correlations by an efficient double-clique condition
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Mining bridging rules between conceptual clusters
Applied Intelligence
Top-N minimization approach for indicative correlation change mining
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Efficient mining of correlated sequential patterns based on null hypothesis
Proceedings of the 2012 international workshop on Web-scale knowledge representation, retrieval and reasoning
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
Non-linear book manifolds: learning from associations the dynamic geometry of digital libraries
Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
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Existing research on mining quantitative databases mainly focuses on mining associations. However, mining associations is too expensive to be practical in many cases. In this paper, we study mining correlations from quantitative databases and show that it is a more effective approach than mining associations. We propose a new notion of Quantitative Correlated Patterns (QCPs), which is founded on two formal concepts, mutual information and all-confidence. We first devise a normalization on mutual information and apply it to QCP mining to capture the dependency between the attributes. We further adopt all-confidence as a quality measure to control, at a finer granularity, the dependency between the attributes with specific quantitative intervals. We also propose a supervised method to combine 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 efficiently mine QCPs by utilizing normalized mutual information and all-confidence to perform a two-level pruning. Our experiments verify the efficiency of QCoMine and the quality of the QCPs.