It all depends on how you built it. DSS offers two options when you deploy clustering models
* Either do a full retrain/recluster each time you run the "clustering recipe". In that situation, the centroids and therefore the definition of the clusters are not "stable", so you can't keep names. You can use a preparation recipe afterwards to name your clusters, but it's possible that the very definition of them will evolve
* Or, deploy a "model" to the Flow (the green losange) and use separate "training" and "scoring" recipes. In that situation, the same centroids are kept between runs, and therefore the names that you set in the Model summary screen are propagated to the output dataset.
Note that this second option is only valid for clustering algorithms that have a notion of centroid (like KMeans)