ClustEval clustering evaluation framework
Hints:

On the right side you see an explanation and more details regarding the clustering quality measure.

## False Discovery Rate

The FDR relates the number of false positives to the total number of positively predicted elements. This estimates a likelihood of an element being negative, if it is predicted positive. This measure is based on the pairwise approach to calculate TP,TN,FP and FN. $FDR = \frac{FP}{FP+TP}$ In the binary classification background we have two classes that we want to distinguish: positive and negative. In this scenario there are four possible outcomes:
• TP (True Positive): The object belongs to class positive and we classified it as positive,
• FP (False Positive ): The object belongs to class negative and we classified it as positive,
• TN (True Negative): The object belongs to class negative and we classified it as negative,
• FN (False Negative): The object belongs to class positive but we classified it as negative
 Reality Positive Negative Prediction Positive TP FP Negative FN TN