Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient search for association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering significant patterns
Machine Learning
Contrast Set Mining for Distinguishing Between Similar Diseases
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
The Journal of Machine Learning Research
Distribution rules with numeric attributes of interest
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Group SAX: extending the notion of contrast sets to time series and multimedia data
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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In this paper we present a technique to derive rules describing contrast sets. Contrast sets are a formalism to represent groups differences. We propose a novel approach to describe directional contrasts using rules where the contrasting effect is partitioned into pairs of groups. Our approach makes use of a directional Fisher Exact Test to find significant differences across groups. We used a Bonferroni within-search adjustment to control type I errors and a pruning technique to prevent derivation of non significant contrast set specializations.