Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
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
Mining optimized association rules for numeric attributes
Journal of Computer and System Sciences
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An evolutionary algorithm to discover numeric association rules
Proceedings of the 2002 ACM symposium on Applied computing
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Density-Based Mining of Quantitative Association Rules
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
An information-theoretic approach to quantitative association rule mining
Knowledge and Information Systems
The Effect of Varying Parameters and Focusing on Bus Travel Time Prediction
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
QuantMiner: a genetic algorithm for mining quantitative association rules
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distribution rules with numeric attributes of interest
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
| Hi-index | 0.00 |
In this paper we propose a framework for defining and discovering optimal association rules involving a numerical attribute A in the consequent. The consequent has the form of interval conditions A, A≥ x or A ∈ I where I is an interval or a set of intervals of the form [x_l,x_u. The optimality is with respect to leverage, one well known association rule interest measure. The generated rules are called Maximal Leverage Rules MLR and are generated from Distribution Rules. The principle for finding the MLR is related to the Kolmogorov-Smirnov goodness of fit statistical test. We propose different methods for MLR generation, taking into account leverage optimallity and readability. We theoretically demonstrate the optimality of the main exact methods, and measure the leverage loss of approximate methods. We show empirically that the discovery process is scalable.