Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
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
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An Evaluation of Approaches to Classification Rule Selection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
Compact fuzzy association rule-based classifier
Expert Systems with Applications: An International Journal
Combined Pattern Mining: From Learned Rules to Actionable Knowledge
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Post-processing of associative classification rules using closed sets
Expert Systems with Applications: An International Journal
Efficient generic association rules based classifier approach
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
An evaluation of heuristics for rule ranking
Artificial Intelligence in Medicine
Building a highly-compact and accurate associative classifier
Applied Intelligence
GARC: a new associative classification approach
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
An Improved Brain Image Classification Technique with Mining and Shape Prior Segmentation Procedure
Journal of Medical Systems
CBC: An associative classifier with a small number of rules
Decision Support Systems
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The integration of supervised classification and association rules for building classification models is not new. One major advantage is that models are human readable and can be edited. However, it is common knowledge that association rule mining typically yields a sheer number of rules defeating the purpose of a human readable model. Pruning unnecessary rules without jeopardizing the classification accuracy is paramount but very challenging. In this paper we study strategies for classification rule pruning in the case of associative classifiers.