Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Bump hunting in high-dimensional data
Statistics and Computing
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Adaptive Directed Acyclic Graphs for Multiclass Classification
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Data mining tasks and methods: Subgroup discovery: deviation analysis
Handbook of data mining and knowledge discovery
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Evaluation Measures for Multi-class Subgroup Discovery
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
RSD: relational subgroup discovery through first-order feature construction
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
The Advantages of Seed Examples in First-Order Multi-class Subgroup Discovery
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Learning multi-class theories in ILP
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Subgroup discovery is concerned with finding subsets of a population whose class distribution is significantly different from the overall distribution. Previously subgroup discovery has been predominantly investigated under the propositional logic framework. This paper investigates multi-class subgroup discovery in an inductive logic programming setting, where subgroups are defined by conjunctions in first-order logic. We present a new weighted covering algorithm, inspired by the Aleph first-order rule learner, that uses seed examples in order to learn diverse, representative and highly predictive subgroups that capture interesting patterns across multiple classes. Our approach experimentally shows considerable and statistically significant improvement of predictive power, both in terms of accuracy and AUC, and theory construction time, by considering fewer hypotheses.