When performing clustering, it is generally recommended to detect outliers. Not doing so could generate very skewed clusters, or many small clusters and one cluster containing almost the whole dataset.

DSS detects outliers by performing a pre-clustering with a large number of clusters and considering the smallest "mini-clusters" as outliers, if:

• their cluster size is less than a specified threshold (ex: 10)
• their cumulative percentage is less than a specified threshold (ex: 1%)

Once outliers are detected, you can either:

• Drop: outliers are dropped.
• Cluster: create a "cluster" from all detected outliers.

Note that this may increase training time significantly.