Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
ACM SIGKDD Explorations Newsletter
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Concise Representation of Frequent Patterns Based on Disjunction-Free Generators
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Analyzing and Predicting Images Through a Neural Network Approach
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Pushing Support Constraints Into Association Rules Mining
IEEE Transactions on Knowledge and Data Engineering
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
Integrating K-Means Clustering with a Relational DBMS Using SQL
IEEE Transactions on Knowledge and Data Engineering
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
Comparing association rules and decision trees for disease prediction
HIKM '06 Proceedings of the international workshop on Healthcare information and knowledge management
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
An efficient incremental mining algorithm-QSD
Intelligent Data Analysis
Models for association rules based on clustering and correlation
Intelligent Data Analysis
Evaluating statistical tests on OLAP cubes to compare degree of disease
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Bayesian Classifiers Programmed in SQL
IEEE Transactions on Knowledge and Data Engineering
Association rule discovery with the train and test approach for heart disease prediction
IEEE Transactions on Information Technology in Biomedicine
Mining association rules with improved semantics in medical databases
Artificial Intelligence in Medicine
A Semi-Supervised Learning Approach to Integrated Salient Risk Features for Bone Diseases
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Association rules and decision trees represent two well-known data mining techniques to find predictive rules. In this work, we present a detailed comparison between constrained association rules and decision trees to predict multiple target attributes. We identify important differences between both techniques for such goal. We conduct an extensive experimental evaluation on a real medical data set to mine rules predicting disease on multiple heart arteries. The antecedent of association rules contains medical measurements and patient risk factors, whereas the consequent refers to the degree of disease on one artery or multiple arteries. Predictive rules found by constrained association rule mining are more abundant and have higher reliability than predictive rules induced by decision trees. We investigate why decision trees miss certain rules, why they tend to have lower confidence and the possibility of improving them to match constrained association rules. Based on our experimental results, we show association rules, compared to decision trees, tend to have higher confidence, they involve larger subsets of the data set, they are better for multiple target attributes, they work better with user-defined binning and they are easier to interpret.