Matrices for statistics
Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
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
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Generalizing the notion of support
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 statistical approach to rule learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Deriving quantitative models for correlation clusters
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining rank-correlated sets of numerical attributes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Polynomial association rules with applications to logistic regression
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering interesting holes in data
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Mining bi-sets in numerical data
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive 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
Finding regional co-location patterns for sets of continuous variables in spatial datasets
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
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The paper presents minimum variance patterns: a new class of itemsets and rules for numerical data, which capture arbitrary continuous relationships between numerical attributes without the need for discretization. The approach is based on finding polynomials over sets of attributes whose variance, in a given dataset, is close to zero. Sets of attributes for which such functions exist are considered interesting. Further, two types of rules are introduced, which help extract understandable relationships from such itemsets. Efficient algorithms for mining minimum variance patterns are presented and verified experimentally.