Detecting Group Differences: Mining Contrast Sets
Data Mining and Knowledge Discovery
Bump hunting in high-dimensional data
Statistics and Computing
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
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This paper analyzes two different approaches to the detection of supporting factors used in descriptive induction. The first is based on the statistical comparison of the pattern properties relative to the properties of the entire negative and the entire positive example sets. The other approach uses artificially generated random examples that are added into the original training set. The methodology is illustrated in the analysis of patients suffering from brain ischaemia.