C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Finding Association Rules That Trade Support Optimally against Confidence
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CTC — Correlating Tree Patterns for Classification
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Correlated pattern mining in quantitative databases
ACM Transactions on Database Systems (TODS)
Cluster-grouping: from subgroup discovery to clustering
Machine Learning
Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach
Journal of Medical Systems
Classification of type-2 diabetic patients by using Apriori and predictive Apriori
International Journal of Computational Vision and Robotics
Tree2: decision trees for tree structured data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Association rule mining to detect factors which contribute to heart disease in males and females
Expert Systems with Applications: An International Journal
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Association rule mining is a data mining technique that reveals interesting relationships in a database Existing approaches employ different parameters to search for interesting rules This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners This paper explores the use of classification performance as a metric for evaluating their output Previous work on forming classifiers from association rules has focussed on accurate classification, whereas we concentrate on using the properties of the resulting classifiers as a basis for comparing confidence-based association rule learners Therefore, we present experimental results on 12 UCI datasets showing that the quality of small rule sets generated by Apriori can be improved by using the predictive Apriori algorithm We also show that CBA, the standard method for classification using association rules, is generally inferior to standard rule learners concerning both running time and size of rule sets.