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


2 Answers 2


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.

  • $\begingroup$ 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
    Commented Nov 25, 2019 at 14:42
  • 1
    $\begingroup$ It labels them as "noise", not as clusters at all. $\endgroup$ Commented Nov 26, 2019 at 23:18

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.

  • $\begingroup$ what do you mean 'remove'? $\endgroup$
    – desertnaut
    Commented Nov 25, 2019 at 23:42

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.