Does anyone have working example code for the Supervised Clustering algorithms (SPAM, SRIDHCR, and SCEC) by Eick et al.? I haven’t been able to find any online.

To some extent yes. But rather than focusing on the true positives of the entire training set, I would be interested in the algorithm carving out subsets of features and values for which precision is very high - higher than the precision of the entire training set. I hope that makes sense?

Hello. I have binary classification problem. However, instead of aiming for a high overall prediction rate for the entire training set, I would like to find subsets of features that with a very high probability places a given sample in category X and other subsets that place samples in category Y. In other words a prediction should not be attempted if the conviction of the estimate is low. Does such an algorithm exist?