In cluster analysis, is it better to normalize the data or standardize it?

In cluster analysis, is it better to normalize to $$[0, 1]$$ (i.e., $$\frac{x-\min(x)}{\max(x)-\min(x)}$$) the data or standardize via z-score (i.e., $$\frac{x-\bar{x}}{s_x}$$) it?

I know normalization remove the influence of outliers, while standarization reshape the distribution of the data to a normal one. Are there cases when is better to use one over the other? For example, when doing (1) Hierarchical Clustering, or (2) Particioning clustering.

• Standardization does not reshape the distribution. There are special rank percentile methods of transform which reshape it onto the normal curve. – ttnphns Oct 17 '19 at 7:53
• Both standardization and the normalization are used and both have their pros and cons. Just look at the formulas themselves and you will be able to answer which method is better (if needed at all) in your particular case of clustering. – ttnphns Oct 17 '19 at 7:59
• Isn't it better to standardize for the sake of computing better euclidean distances? – Nip Oct 17 '19 at 15:48