When is centering and scaling needed before doing hierarchical clustering?
My question is related to the centering and scaling, Is it absolutely necessary to both scale and center the data?, even in scenarios where all the data is coming from the same platform and with same units of measurement.


It depends on the type of data you have. For some types of well defined data, there may be no need to scale and center. A good example is geolocation data (longitudes and latitudes). If you were seeking to cluster towns, you wouldn't need to scale and center their locations.

For data that is of different physical measurements or units, its probably a good idea to scale and center. For example, when clustering vehicles, the data may contain attributes such as number of wheels, number of doors, miles per gallon, horsepower etc. In this case it may be a better idea to scale and center since you are unsure of the relationship between each attribute.

The intuition behind that is that since many clustering algorithms require some definition of distance, if you do not scale and center your data, you may give attributes which have larger magnitudes more importance.

In the context of your problem, I would scale and center the data if it contains attributes like patient height, weight, age etc.

This answer on a similar question has more.