Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A greedy classification algorithm based on association rule
Applied Soft Computing
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
A Lazy Approach to Associative Classification
IEEE Transactions on Knowledge and Data Engineering
A Novel Rule Ordering Approach in Classification Association Rule Mining
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
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Classification is one of the important issues in data mining. Past studies show that associative classification outperforms traditional classification techniques. The rule selection process, particularly the rule pruning mechanism, in an associative classifier generally plays an important role in classification accuracy. Most associative classifiers, such as CBA, CPAR and etc., keep only a rule of the highest confidence among conflict rules. In this paper, we propose a rule selection method called ICRP to further improve the classification accuracy. ICRP contains three rule pruning mechanisms, general-rule pruning, conflict rule pruning, and k-data coverage pruning, for preserving conflict but useful rules. The classifier has the best accuracy when the minimum support is 1%, minimum confidence is 40%, conflict threshold is 30%, and k is 2. Comprehensive experiments using the 30 well-known UCI datasets show that ICRP outperforms CPAR by approximately 7% accuracy, CBA by approximately 5.93% and CMAR by approximately 1.88%.