Clustering of sparse matrix with many co-variates
You need to apply PCA and reduce your data to lower dimensions, then applying a classic clustering technique (e.g. k-means or DBSCAN) works depending on how your samples are distributed. I strongly recommend you visualize data in 2 or 3 dimensions and have a brief visual inspection. Gives you an insight about what is going on there. However the final number of dimensions you get out of PCA might be chosen to be more than 2 or 3 (it is usually).

Steps


Normalize features if needed
Apply PCA and take the number of PCs which explains 85% of variance
Optional: Visualize embedded data in 2 or 3 dimension to get a feeling about distribution of samples (it does not anything more than just an intuition! Even that intuition can not be validated)
Apply a clustering algorithm on the result of (2)