Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 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
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Relative Unsupervised Discretization for Regresseion Problems
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Optimized Support Rules for Numeric Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Towards a Simple Clustering Criterion Based on Minimum Length Encoding
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Interesting Fuzzy Association Rules in Quantitative Databases
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Discovering Associations between Spatial Objects: An ILP Application
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Mining relational association rules for propositional classification
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Mining and filtering multi-level spatial association rules with ARES
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
| Hi-index | 0.00 |
The paper describes a context-sensitive discretization algorithm that can be used to completely discretize a numeric or mixed numeric-categorical dataset. The algorithm combines aspects of unsupervised (class-blind) and supervised methods. It was designed with a view to the problem of finding association rules or functional dependencies in complex, partly numerical data. The paper describes the algorithm and presents systematic experiments with a synthetic data set that contains a number of rather complex associations. Experiments with varying degrees of noise and "fuzziness" demonstrate the robustness of the method. An application to a large real-world dataset produced interesting preliminary results, which are currently the topic of specialized investigations.