Do Clustering algorithms need feature scaling in the pre-processing stage?
Clustering algorithms are certainly effected by the feature scaling. 

Example: 

Let's say that you have two features:


weight (in Lbs)
height (in Feet)


... and we are using these to predict whether a person needs a 'S' or 'L' size shirt.

We are using weight+height for that, and in our trained set let's say we have two people already in clusters: 


Adam (175Lbs+5.9ft) in 'L'
Lucy (115Lbs+5.2ft) in 'S'. 


We have a new person - Alan (140Lbs+6.1ft.), and your clustering algo will put it in the cluster which is nearest.  So, if we don't scale the features here, the height is not having much effect and Alan will be allotted in 'S' cluster.

So, we need to scale it. Scikit Learn provides many functions for scaling. One you can use is sklearn.preprocessing.MinMaxScaler.