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I am wondering what's the best way to handle outliers when using non-supervised clustering algorithms? Thanks!

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If you have outliers, the best way is to use a clustering algorithm that can handle them.

For example DBSCAN clustering is robust against outliers when you choose minpts large enough. Don't use k-means: the squared error approach is sensitive to outliers. But there are variants such as k-means-- for handling outliers.

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  • $\begingroup$ Thanks! So does DBSCAN make each outlier a separated cluster? Then we can remove clusters with very small sizes. Or what is the mechanism DBSCAN uses to identify the outliers? $\endgroup$ – Edamame Nov 25 '19 at 14:42
  • $\begingroup$ It labels them as "noise", not as clusters at all. $\endgroup$ – Has QUIT--Anony-Mousse Nov 26 '19 at 23:18
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you can perform standardization of your data using Standard Scaler before applying clustering techniques or you can use k-mediod clustering algorithm. You can also use z-score analysis to remove your outliers.

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  • $\begingroup$ what do you mean 'remove'? $\endgroup$ – desertnaut Nov 25 '19 at 23:42

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