clustering with heterogeneous (quantitative and qualitative)data?
K-means algorithm cannot be directly used for data with both numerical and categorical values because of its objective function. K-means uses Euclidean distance, which cannot be defined for categorical data. 

However, there is a simple method which combines Euclidean distance and Hamming distance for the finding the similarity between instances that include both numerical and categorical features. In this method Euclidean distance is used as a metric for computing the similarity between numerical data and hamming distance for computing the similarity between categorical data. Both of them with the appropirate weights form an objective function that can handle mixed data.

For more information check the relevant paper

https://grid.cs.gsu.edu/~wkim/index_files/papers/kprototype.pdf