Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining in Large Databases Using Domain Generalization Graphs
Journal of Intelligent Information Systems
Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Knowledge Discovery Using Domain Generalization Graphs
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Heuristic Measures of Interestingness
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Market Basket Data Using Share Measures and Characterized Itemsets
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Evaluation of Interestingness Measures for Ranking Discovered Knowledge
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Domain-Driven Local Exceptional Pattern Mining for Detecting Stock Price Manipulation
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Mining interesting infrequent and frequent itemsets based on minimum correlation strength
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Study of positive and negative association rules based on multi-confidence and chi-squared test
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
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One of the most important steps in any knowledge discovery task is the interpretation and evaluation of discovered patterns. To address this problem, various techniques, such as the chi-square test for independence, have been suggested to reduce the number of patterns presented to the user and to focus attention on those that are truly statistically significant. However, when mining a large database, the number of patterns discovered can remain large even after adjusting significance thresholds to eliminate spurious patterns. What is needed, then, is an effective measure to further assist in the interpretation and evaluation step that ranks the interestingness of the remaining patterns prior to presenting them to the user. In this paper, we describe a two-step process for ranking the interestingness of discovered patterns that utilizes the chi-square test for independence in the first step and objective measures of interestingness in the second step. We show how this two-step process can be applied to ranking characterized/generalized association rules and data cubes.