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: concepts and techniques
Data mining: concepts and techniques
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
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
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Using association rules to make rule-based classifiers robust
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
Threshold tuning for improved classification association rule mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining frequent itemsets from multidimensional databases
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Interestingness measures for classification based on association rules
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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The traditional methods for mining classification rules such as heuristics or greedy methods only generate the rules that are too general or overfitting to do with the given database. Thus, they introduce high error ratio. Recently, a new method of mining classification rules is proposed: classification rules mining based on association rules (CARs). It is more advantageous than the traditional methods in that it removes noise and therefore the accuracy is higher. In this paper, we propose ECR-CARM algorithm. It is based on ECR-tree to find all CARs. Besides that, it is necessary for redundant rules pruning and rules reducing to gain the smaller rules set (i.e., reducing the time of identifying the class of new cases and increasing the accuracy). We also develop property to fast prune rules.