Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A multistrategy approach to classification learning in databases
Data & Knowledge Engineering
Multi-level organization and summarization of the discovered rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Improving an Association Rule Based Classifier
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Intuitive Representation of Decision Trees Using General Rules and Exceptions
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Machine Learning and Its Applications, Advanced Lectures
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
ART: A Hybrid Classification Model
Machine Learning
Taking class importance into account
ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
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Separate-and-conquer classifiers strongly depend on the criteria used to choose which rules will be included in the classification model. When association rules are employed to build such classifiers (as in ART [3]), rule evaluation can be performed attending to different criteria (other than the traditional confidence measure used in association rule mining). In this paper, we analyze the desirable properties of such alternative criteria and their effect in building rule-based classifiers using a separate-and-conquer strategy.