Classification based on a Clustering Result
I think you need to do some Feature Engineering, i.e., as you explained in the question, those values mean something to your application.

For example : 1-3 : Bad, 4-6 : Average, 7-10 : Good

V1 new_V1
3   Bad
5   Average
7   Good


Something like this, so that clustering algorithm can make sense out of it.

Assuming that you don't have any constraint in using R.
If you have both Numeric and Categorical variable(as above) then you can usethis package : ClustMixType, it can understand and do the clustering based on the data fed. As traditional K Means algorithm is not applicable here as it works only for Numeric Data.

If there is any discrepancy in data you can convert them explicitly by 
new_V1 &lt;- as.factor(new_V1); 
if it is a numeric variable V2 &lt;- as.numeric(V2) before feeding the data to Clustering algorithm.

Once you get it, then you can use this package: Bruota, use this package to get Predictor Importance(what all variables are important). This can be done only when you know the Target Variable, Most likely your target variable would be the Clustering outcome.

Implementation is over here, please follow this Link

Most likely you would end up getting your desired result, Do let me know if you have any additional questions.