# How to include noise in clustering evaluation?

When evaluating clustering methods which do have a definition for noise points (like dbscan), how noise will affect evaluation?

Consider a clean dataset like well known Iris dataset. There is no noise in it. In evaluation metrics like homogeneity should we remove noise labels and then compute the metric?

Ex:

True_labels = [1, 1, 1, 2, 2, 1]
Expected_labels = [a, a, -1, b, -1]


Do we need to alter these labels some how like this?

True_labels = [1, 1, 2]
Expected_labels = [a, a, b]