Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Adapting classification rule induction to subgroup discovery
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Learning predictive clustering rules
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Maximal exceptions with minimal descriptions
Data Mining and Knowledge Discovery
From black and white to full color: extending redescription mining outside the Boolean world
Statistical Analysis and Data Mining
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Biomedical experimental data sets may often include many features both at input (description of cases, treatments, or experimental parameters) and output (outcome description). State-of-the-art data mining techniques can deal with such data, but would consider only one output feature at the time, disregarding any dependencies among them. In the paper, we propose the technique that can treat many output features simultaneously, aiming at finding subgroups of cases that are similar both in input and output space. The method is based on k -medoids clustering and analysis of contingency tables, and reports on case subgroups with significant dependency in input and output space. We have used this technique in explorative analysis of clinical data on femoral neck fractures. The subgroups discovered in our study were considered meaningful by the participating domain expert, and sparked a number of ideas for hypothesis to be further experimentally tested.