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
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
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining optimized association rules for numeric attributes
Journal of Computer and System Sciences
Multiple Comparisons in Induction Algorithms
Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Using metarules to organize and group discovered association rules
Data Mining and Knowledge Discovery
Mining long high utility itemsets in transaction databases
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Efficient mining of salinity and temperature association rules from ARGO data
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
MPSQAR: Mining Quantitative Association Rules Preserving Semantics
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Mining long high utility itemsets in transaction databases
WSEAS Transactions on Information Science and Applications
Expert Systems with Applications: An International Journal
Fast Subgroup Discovery for Continuous Target Concepts
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
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
An efficient algorithm for finding dense regions for mining quantitative association rules
Computers & Mathematics with Applications
Interestingness of Association Rules Using Symmetrical Tau and Logistic Regression
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
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
A tool for interactive subgroup discovery using distribution rules
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Integrated Computer-Aided Engineering
Re-mining positive and negative association mining results
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
A statistical interestingness measures for XML based association rules
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Interestingness measures for association rules based on statistical validity
Knowledge-Based Systems
Application of data mining in relationship between water quantity and water quality
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Re-mining item associations: Methodology and a case study in apparel retailing
Decision Support Systems
Distribution rules with numeric attributes of interest
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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
Multivariate discretization for associative classification in a sparse data application domain
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Integrating quantitative attributes in hierarchical clustering of transactional data
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
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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Association rules are a key data-mining tool and as such have been well researched. So far, this research has focused predominantly on databases containing categorical data only. However, many real-world databases contain quantitative attributes and current solutions for this case are so far inadequate. In this paper we introduce a new definition of quantitative association rules based on statistical inference theory. Our definition reflects the intuition that the goal of association rules is to find extraordinary and therefore interesting phenomena in databases. We also introduce the concept of sub-rules which can be applied to any type of association rule. Rigorous experimental evaluation on real-world datasets is presented, demonstrating the usefulness and characteristics of rules mined according to our definition.