Let's say I have a reasonable amount of features (~30). My understanding is, that features included represent the task defined by the data scientist. Thus, by using different set of features, I might be actually exploring very different concepts inside the data. I understand removing redundant features thought, since it might help the interpretability and does not change clustering result.