C4.5: programs for machine learning
C4.5: programs for machine learning
Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Improving Associative Classification by Incorporating Novel Interestingness Measures
ICEBE '05 Proceedings of the IEEE International Conference on e-Business Engineering
Obtaining Best Parameter Values for Accurate Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A review of associative classification mining
The Knowledge Engineering Review
Building Accurate Associative Classifier Based on Closed Itemsets and Certainty Factor
IITAW '09 Proceedings of the 2009 Third International Symposium on Intelligent Information Technology Application Workshops
A study on interestingness measures for associative classifiers
Proceedings of the 2010 ACM Symposium on Applied Computing
Mining closed itemsets in data stream using formal concept analysis
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
Building a highly-compact and accurate associative classifier
Applied Intelligence
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Associative classification aims to discover a set of constrained association rules, called Class Association Rules (CARs). The consequent of a CAR is a singleton and is restricted to be a class label. Traditionally, the classifier is built by selecting a subset of CARs based on some interestingness measure. The proposed approach for associative classification, called Associative Classifier based on Closed Itemsets (ACCI), scans the dataset only once and generates a set of CARs based on closed itemsets (ClosedCARs) using a lattice based data structure. Subsequently, rule conflicts are removed and a subset of non-conflicting ClosedCARs which covers the entire training set is chosen as a classifier. The entire process is independent of the interestingness measure. Experimental results on benchmark datasets from UCI machine repository reveal that the achieved classifiers are more accurate than those built using existing approaches.