Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multivariate discretization of continuous variables for set mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Approximation algorithms
On Finding Optimal Discretizations for Two Attributes
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Multivariate supervised discretization, a neighborhood graph approach
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Multisplitting Revisited: Optima-Preserving Elimination of Partition Candidates
Data Mining and Knowledge Discovery
A multivariate discretization method for learning Bayesian networks from mixed data
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Approximation algorithms for minimizing empirical error by axis-parallel hyperplanes
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Discretization of the value range of a numerical feature is a common task in data mining and machine learning. Optimal multivariate discretization is in general computationally intractable. We have proposed approximation algorithms with performance guarantees for training error minimization by axis-parallel hyperplanes. This work studies their efficiency and practicability. We give efficient implementations to both greedy set covering and linear programming approximation of optimal multivariate discretization. We also contrast the algorithms empirically to an efficient heuristic discretization method.