Dimensionality reduction reduces the number of variables by arranging them into 'principal components' grouping together all correlated variables. The principal components are computed to carry as much variance as possible from the original dataset.

The main interest of using PCA for clustering is to improve the running time of the algorithms, especially when you have a large number of dimensions.

You can choose to enable it, disable it, or try both options to compare.