T-SNE is a manifold technique and as such does not preserve distances; therefore it is not recommended to run distance-based (e.g. k-means) or density-based (e.g. DBSCAN) clustering algorithms on the output of T-SNE. This has been asked before.
If you want a dimensional reduction algorithm that does preserve distances, you can use PCA instead of T-SNE. PCA gives you an orthogonal rotation of your original data; one of the properties on an orthogonal transformation is that it preserves distances. When you use PCA for dimensional reduction by projecting into a lower dimensional space by throwing out factors with small eigenvectors, you lose only a small amount of information about distance.