Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Information Retrieval
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Metric for Selection of the Most Promising Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Machine Learning of Credible Classifications
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
CCAIIA: Clustering Categorial Attributed into Interseting Accociation Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Peculiarity Oriented Multidatabase Mining
IEEE Transactions on Knowledge and Data Engineering
Evaluation of rule interestingness measures with a clinical dataset on hepatitis
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Using Information-Theoretic Measures to Assess Association Rule Interestingness
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Rule Evaluation Support Method with Learning Models Based on Objective Rule Evaluation Indexes
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
A data analysis approach for evaluating the behavior of interestingness measures
DS'05 Proceedings of the 8th international conference on Discovery Science
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To support data mining post-processing, which is one of the important procedures in a data mining process, at least 40 indices are proposed to acquire valuable knowledge. However, since their behaviors have never been elucidated, domain experts are required to spend their time to understanding the meanings of each index in a given data mining result. In this paper, we present an analysis of the behavior of objective rule evaluation indices on classification rule sets by principle component analysis (PCA). Therefore, we carried out a PCA to a dataset consisting of the 39 objective rule evaluation indices. In order to obtain the dataset, we calculated the average values of the bootstrap method on 32 classification rule sets learned by information gain ratio. Then, we identified the seven functional groups of the objective indices based on the PCA. Using this result, we discuss a rule evaluation interface for use by human experts.