C4.5: programs for machine learning
C4.5: programs for machine learning
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
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multivariate discretization for set mining
Knowledge and Information Systems
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Relative Unsupervised Discretization for Association Rule Mining
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Toward Unsupervised Correlation Preserving Discretization
IEEE Transactions on Knowledge and Data Engineering
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Most discretization algorithms are univariate and consider only one attribute at a time. Stephen D. Bay presented a multivariate discretization(MVD) method that considers the affects of all the attributes in the procedure of data mining. But as the author mentioned, any test of differences has a limited amount of power. We present OMVD by improving MVD on the power of testing differences with a genetic algorithm. OMVD is more powerful than MVD because the former does not suffer from setting the difference threshold and from seriously depending on the basic intervals. In addition, the former simultaneously searches partitions for multiple attributes. Our experiments with some synthetic and real datasets suggest that OMVD could obtain more interesting discretizations than could MVD.