Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Characterizing Model Erros and Differences
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mining Changes for Real-Life Applications
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Speed-up Iterative Frequent Itemset Mining with Constraint Changes
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting Source Code Changes by Mining Change History
IEEE Transactions on Software Engineering
Mining changes in customer buying behavior for collaborative recommendations
Expert Systems with Applications: An International Journal
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
Measuring the uncertainty of differences for contrasting groups
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
On the stimulation of patterns: definitions, calculation method and first usages
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
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Mining group differences is useful in many applications, such as medical research, social network analysis and link discovery. The differences between groups can be measured from either statistical or data mining perspective. In this paper, we propose an empirical likelihood (EL) based strategy of building confidence intervals for the mean and distribution differences between two contrasting groups. In our approach we take into account the structure (semi-parametric) of groups, and experimentally evaluate the proposed approach using both simulated and real-world data. The results demonstrate that our approach is effective in building confidence intervals for group differences such as mean and distribution function.