I am looking for a clustering procedure that will group a number of 3D points on the basis of their spatial relations and multivariate dimensions. Dimensions are mostly represented with (interval) metric variables and few composed of categorical variables.
My questions:

1. Is there any clustering procedure that also factors in weighting coefficients assigned (by user) to certain dimensions or spatial relation (distance)?
2. Is there any clustering procedure that analyses both categorical and metric dimensions?
3. Is there a clustering procedure thatr fullfill these requirements and it is already implemented in any of the commonly-used statistical software such as R, MATLAB, Python?

Thanks

I am looking for a clustering procedure that will group a number of 3D points on the basis of their spatial relations and multivariate dimensions. Dimensions are mostly represented with (interval) metric variables and few composed of categorical variables.
My questions:

1. Is there any clustering procedure that also factors in weighting coefficients assigned (by user) to certain dimensions or spatial relation (distance)?
2. Is there any clustering procedure that analyses both categorical and metric dimensions?
3. Is there a clustering procedure thatr fullfill these requirements and it is already implemented in any of the commonly-used statistical software such as R, MATLAB, Python?

My data looks like this:

1. 0.0303 0.0763 0.1363 0.1753 0.1916 0.2411 0.2661
2. 0.0000 0.0000 0.0000 0.0084 0.0176 0.0393 0.0482
3. 0.3287 0.3794 0.4887 0.7151 1.0220 4.8060 7.4140
4. 0.2310 0.2692 0.3563 0.4384 0.4836 0.6694 0.8040

Is there a method that considers both numerical and alphabetical values? For example if I add to this dataset a column with values such as A, B, C, AC, CF,.... Thanks