C4.5: programs for machine learning
C4.5: programs for machine learning
Measuring lift quality in database marketing
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
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
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Lazy Associative Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
A Novel Classification Algorithm Based on Association Rules Mining
Knowledge Acquisition: Approaches, Algorithms and Applications
Calibrated lazy associative classification
Information Sciences: an International Journal
Interestingness measures for association rules: Combination between lattice and hash tables
Expert Systems with Applications: An International Journal
ACME: an associative classifier based on maximum entropy principle
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
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This paper proposes a new algorithm for classification based on association rule with interestingness measures. The proposed algorithm uses a tree structure for maintenance of related information in each node, thus making the process of generating rules fast. Besides, the proposed algorithm can be easily extended to integrate some measures together for ranking rules. Experiments are also made to show the efficiency of the proposed approach for different settings. The mining time for different interestingness measures is varied only a little when ten measures are integrated.