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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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    $\begingroup$ 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. $\endgroup$ Sep 1, 2014 at 13:31
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    $\begingroup$ what do you plan to do next with the clustering result? $\endgroup$ Sep 1, 2014 at 16:06
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    $\begingroup$ But what do you do when you have found a cluster? You'll never know if a cluster is good when you stop after clustering. $\endgroup$ Sep 1, 2014 at 17:58
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    $\begingroup$ Assume your validation was good for now. What do you plan to do next? How do you use the result? $\endgroup$ Sep 1, 2014 at 18:22
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    $\begingroup$ But it could as well be just one cluster per client IP, then you did not gain anything. I haven't seen any convincing mining on IP data yet... you'll have to try this "sth like that" part, to see if your clustering helps. $\endgroup$ Sep 1, 2014 at 20:43

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