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.
- Is there any clustering procedure that also factors in weighting coefficients assigned (by user) to certain dimensions or spatial relation (distance)?
- Is there any clustering procedure that analyses both categorical and metric dimensions?
- 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:
- 0.0303 0.0763 0.1363 0.1753 0.1916 0.2411 0.2661
- 0.0000 0.0000 0.0000 0.0084 0.0176 0.0393 0.0482
- 0.3287 0.3794 0.4887 0.7151 1.0220 4.8060 7.4140
- 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