Non-negative Matrix Factorization for clustering

I'm learning to user NMF to do clustering. Based on the reading What is a good explanation of Non Negative Matrix Factorization? and https://iksinc.online/2016/03/21/what-is-nmf-and-what-can-you-do-with-it/. The first link mention for data preprocessing, normalization is necessary. My question is if we do a normalization for the features there will be negative values in the data. Isn't non-negative necessary for this method?

For NMF,https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html , it seems you still need to define n_component before hand. Does it depend on domain knowledge or any process to help to select? There is one constraint that n_component will be far less than matrix A(m by n) m or n.