Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysing customer Churn in insurance data: a case study
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Sampling-based sequential subgroup mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
Exploiting background knowledge for knowledge-intensive subgroup discovery
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Subgroup discovery is the task of identifying the top k patterns in a database with most significant deviation in the distribution of a target attribute Y. Subgroup discovery is a popular approach for identifying interesting patterns in data, because it combines statistical significance with an understandable representation of patterns as a logical formula. However, it is often a problem that some subgroups, even if they are statistically highly significant, are not interesting to the user. We present an approach based on the work on ranking Support Vector Machines that ranks subgroups with respect to the user's concept of interestingness, and finds more interesting subgroups. This approach can significantly increase the quality of the subgroups.