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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]
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There are multiple choices. For example, you could treat all noise as a single cluster, or each object as being its own 1-element cluster.

I don't think any of the evaluation methods works very well in these cases though. Evaluation of clusterings is pretty much broken by design, unfortunately.

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  • $\begingroup$ Clustering illness define and evaluation is breaking my bones at the moment! $\endgroup$ – Mehraban Sep 1 '14 at 13:29
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    $\begingroup$ Try a problem-driven approach. Don't solve math, solve real data problems; don't cluster random data, but try to solve some real world problem. $\endgroup$ – Has QUIT--Anony-Mousse Sep 1 '14 at 13:31
  • $\begingroup$ Can we set a time to chat about this? $\endgroup$ – Mehraban Sep 1 '14 at 13:46
  • $\begingroup$ I can't help you that much - I don't have a code or formula solution; but the idea is to continue as if your clustering result was good, and then look back after the next step, if you need to go back and retry. $\endgroup$ – Has QUIT--Anony-Mousse Sep 1 '14 at 13:53
  • $\begingroup$ I don't meant to ask for formula or code. I most want to know about the way I need to do the whole thing. $\endgroup$ – Mehraban Sep 1 '14 at 13:56

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