I'm in the middle of a result analysis for some clustering methods, doing quality tests for different clustering outputs coming from a singular input dataset where data preprocessing and cleaning methods are swapped.
So far, the clustering outputs from dataset where any outlier detection technique has been applied show a poor performance. Hence, I was wondering whether it's worth at all applying an outlier detection technique for clustering. My particular results say it isn't, but I'd like to know your opinions from a wider perspective.
If needed, the clustering methods used are: K-means, SOM maps and hierarchical clustering. Thanks!!