Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Discovery of Association Rules in Tabular Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficient Search of Reliable Exceptions
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Exception Rule Mining with a Relative Interestingness Measure
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Rule Evaluation Measures: A Unifying View
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
On a confidence gain measure for association rule discovery and scoring
The VLDB Journal — The International Journal on Very Large Data Bases
The discovery of association rules from tabular databases comprising nominal and ordinal attributes
Intelligent Data Analysis
A fast pruning redundant rule method using Galois connection
Applied Soft Computing
Interestingness measures for association rules based on statistical validity
Knowledge-Based Systems
Reliable representations for association rules
Data & Knowledge Engineering
Interestingness measures for fixed consequent rules
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
| Hi-index | 0.00 |
Association rule mining can provide genuine insight into the data being analysed; however, rule sets can be extremely large, and therefore difficult and time-consuming for the user to interpret. We propose reducing the size of Apriori rule sets by removing overlapping rules, and compare this approach with two standard methods for reducing rule set size: increasing the minimum confidence parameter, and increasing the minimum antecedent support parameter. We evaluate the rule sets in terms of confidence and coverage, as well as two rule interestingness measures that favour rules with antecedent conditions that are poor individual predictors of the target class, as we assume that these represent potentially interesting rules. We also examine the distribution of the rules graphically, to assess whether particular classes of rules are eliminated. We show that removing overlapping rules substantially reduces rule set size in most cases, and alters the character of a rule set less than if the standard parameters are used to constrain the rule set to the same size. Based on our results, we aim to extend the Apriori algorithm to incorporate the suppression of overlapping rules.