Kmeans - Does removing outliers on a dimension affect other dimensions clustering prediction? I have a set with several features that I wish to cluster using Kmeans. If I remove a point that is an outlier on one dimenssion but not in the others will it affect the result?
Outliers were found using Tukey method on every dimension.
 A: It probably will but that is why you are removing it in the first place? Since kmean is distance/similarity based, I expect it will affect the result but in a positive way.
A: K-means is sensitive to outliers. These will often become 1-element clusters etc., So removing them can be a good idea.
In general, k-means makes most sense when you have lots of data, enough to reliably estimate centers. Then removing a few points completely should not make much of a difference. You can add back the removed points later and check how much they would affect the means.
If you have noisy data, it is however not clear that the overall assumptions of k-means hold (every point belongs to exactly one cluster; clusters have the same extends). On noisy data methods such as DBSCAN tend to work better that have an integrated concept of "noise", and which separate clusters by density, not by distance from the centers.
There are also k-means variants that have built-in functionality to ignore outliers. You may want to try these; the standard k-means is actually one of the worst choices.
