A greedy classification algorithm based on association rule
Applied Soft Computing
Learning rules with negation for text categorization
Proceedings of the 2007 ACM symposium on Applied computing
A review of associative classification mining
The Knowledge Engineering Review
Data & Knowledge Engineering
Mining the data from a hyperheuristic approach using associative classification
Expert Systems with Applications: An International Journal
An association-based case reduction technique for case-based reasoning
Information Sciences: an International Journal
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
An Agent-Oriented Data Mining Framework for Mass Customization in the Automotive Industry
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A Novel Classification Algorithm Based on Association Rules Mining
Knowledge Acquisition: Approaches, Algorithms and Applications
Constructing Associative Classifier Using Rough Sets and Evidence Theory
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Expert Systems with Applications: An International Journal
CIMDS: adapting postprocessing techniques of associative classification for malware detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
Intelligent phishing detection system for e-banking using fuzzy data mining
Expert Systems with Applications: An International Journal
Fuzzy multiple support associative classification approach for prediction
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Associative classification in the prediction of tuberculosis
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Categorize arabic data sets using multi-class classification based on association rule approach
Proceedings of the 2011 International Conference on Intelligent Semantic Web-Services and Applications
Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems
Expert Systems with Applications: An International Journal
Multivariate discretization for associative classification in a sparse data application domain
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Classification based on specific rules and inexact coverage
Expert Systems with Applications: An International Journal
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
Improving the performance of association classifiers by rule prioritization
Knowledge-Based Systems
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Improving classification accuracy of associative classifiers by using k-conflict-rule preservation
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
ACNB: Associative Classification Mining Based on Naïve Bayesian Method
International Journal of Information Technology and Web Engineering
CAR-NF: A classifier based on specific rules with high netconf
Intelligent Data Analysis
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Summary form only given. Constructing fast, accurate classifiers for large data sets is an important task in data mining and knowledge discovery. In this research paper, a new classification method called multi-class classification based on association rules (MCAR) is presented. MCAR uses an efficient technique for discovering frequent items and employs a rule ranking method which ensures detailed rules with high confidence are part of the classifier. After experimentation with fifteen different data sets, the results indicated that the proposed method is an accurate and efficient classification technique. Furthermore, the classifiers produced are highly competitive with regards to error rate and efficiency, if compared with those generated by popular methods like decision trees, RIPPER and CBA.