Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Inductive Learning Algorithms for Complex Systems Modeling
Inductive Learning Algorithms for Complex Systems Modeling
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Machine Learning
Machine Learning
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
A fast calculation of metric scores for learning Bayesian network
International Journal of Automation and Computing
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This paper is about searching the combinatorial space of contingency tables during the inner loop of a nonlinear statistical optimization. Examples of this operation in various data analytic communities include searching for nonlinear combinations of attributes that contribute significantly to a regression (Statistics), searching for items to include in a decision list (machine learning) and association rule hunting (Data Mining). This paper investigates a new, efficient approach to this class of problems, called RADSEARCH (Real-valued All-Dimensions-tree Search). RADSEARCH finds the global optimum, and this gives us the opportunity to empirically evaluate the question: apart from algorithmic elegance what does this attention to optimality buy us? We compare RADSEARCH with other recent successful search algorithms such as CN2, PRIM, APriori, OPUS and DenseMiner. Finally, we introduce RADREG, a new regression algorithm for learning real-valued outputs based on RADSEARCHing for highorder interactions.