ClustEval clustering evaluation framework
Hints:

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

## Jaccard Index (R)

The Jaccard Index neglects the true negatives (TN) and relates the true positives to the number of pairs that either belong to the same class or are in the same cluster. This measure estimates a likelihood of an element being positive, if it is not correctly classified a negative element. This measure is based on the pairwise approach to calculate TP,TN,FP and FN. $J = \frac{TP}{TP+FP+FN}$ 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