Scalable algorithms for mining large databases
KDD '99 Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Improved approximation algorithms for rectangle tiling and packing
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Mining Optimized Gain Rules for Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
Relative Unsupervised Discretization for Association Rule Mining
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Finding the most interesting correlations in a database: how hard can it be?
Information Systems
Multi-level fuzzy mining with multiple minimum supports
Expert Systems with Applications: An International Journal
QuantMiner: a genetic algorithm for mining quantitative association rules
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An algorithm to mine general association rules from tabular data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Discovering fuzzy inter- and intra-object associations
Expert Systems with Applications: An International Journal
Discovering and managing quantitative association rules
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we generalize the optimized support association rule problem by permitting rules to contain disjunctions over uninstantiated numeric attributes. For rules containing a single numeric attribute, we present a dynamic programming algorithm for computing optimized association rules. Furthermore, we propose a bucketing technique for reducing the input size, and a divide and conquer strategy that improves the performance significantly without sacrificing optimality. Our experimental results for a single numeric attribute indicate that our bucketing and divide and conquer enhancements are very effective in reducing the execution times and memory requirements of our dynamic programming algorithm. Furthermore, they show that our algorithms scale up almost linearly with the attribute's domain size as well as the number of disjunctions.