Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Interestingness measures for fixed consequent rules
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Making comparisons from the post-processing of association rules have become a research challenge in data mining. By evaluating interestingness value calculated from interestingness measures on association rules, a new approach based on the Pearson’s correlation coefficient is proposed to answer the question: How we can capture the stable behaviors of interestingness measures on different datasets?. In this paper, a correlation graph is used to evaluate the behavior of 36 interestingness measures on two datasets.