Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth 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)
Finding Functional Groups of Objective Rule Evaluation Indices Using PCA
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
A Comparison of Composed Objective Rule Evaluation Indices Using PCA and Single Indices
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Improving a rule evaluation support method based on objective indices
International Journal of Advanced Intelligence Paradigms
Analyzing correlation coefficients of objective rule evaluation indices on classification rules
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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In recent years, the problem of finding the different aspects existing in a dataset has attracted many authors in the domain of knowledge quality in KDD. The discovery of knowledge in the form of association rules has become an important research. One of the most difficult issues is that an enormous number of association rules are discovered, so it is not easy to choose the best association rules or knowledge for a given dataset. Some methods are proposed for choosing the best rules with an interestingness measure or matching properties of interestingness measure for a given set of interestingness measures. In this paper, we propose a new approach to discover the clusters of interestingness measures existing in a dataset. Our approach is based on the evaluation of the distance computed between interestingness measures. We use two techniques: agglomerative hierarchical clustering (AHC) and partitioning around medoids (PAM) to help the user graphically evaluates the behavior of interestingness measures.