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
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
On detecting differences between groups
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Association rule mining: models and algorithms
Association rule mining: models and algorithms
COSINE: a vertical group difference approach to contrast set mining
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
GENCCS: a correlated group difference approach to contrast set mining
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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Negative contrast sets show that the relationships between the existence of some characteristics and the nonexistence of some other characteristics for various groups are significantly different. These sets can provide additional information for making decisions. Mining positive contrast sets like 'X and Y' is relatively straightforward and computationally efficient with respect to mining negative contrast sets like 'not X and Y'. In this paper, we propose an algorithm to discover negative contrasts sets across groups, and establish some properties to accelerate the execution of the algorithm. It is then applied on an insurance data set to find meaningful negative contrast sets that can assist in designing insurance programs for various types of customers.