Remove noise by clustering on which step of pre-processing is better?
Looking at your different steps, the important thing to do is check which step would be affected by outliers.


Removing missing values is not affected because this step is not dependent on other data points present (or not) in the dataset.
However, normalizing your data is. Indeed, let's say your outliers contain extreme values, this will affect the normalized values of the non-outlier data points.


Therefore, intuitively, I would perform your noise removal at the very start or after step 1.

Ultimately, you should see what works better for your task. Perhaps removing outliers doesn't help as much as you'd expect. Same with your pre-processing. Feel free to experiment!