Clustering using both text and numerical features
One way to achieve this is to use a clustering method based on a custom similarity/distance measure. For example you could defined the similarity measure between two instances as:
$$sim(\langle v_1, n_1\rangle,\langle v_2, n_2\rangle)=\frac{1}{2} cosine(v_1,v_2)\ +\ \frac{1}{2} \left(1-\frac{|n_1-n_2|}{max(n_1,n_2)}\right)$$
This measure gives the same weight to the similarity between the vectors ($v_1$ and $v_2$) and the similarity between the numerical values ($n_1$ and $n_2$). Note that since this similarity measure is normalized, you can convert it to a normalized distance measure: $d=1-s$.
Of course you should define the exact measure based on what the values represent, this is just an example.
You could use this measure with a hierarchical clustering method or a graph clustering method (with edges based on similarity value).