Which of the following is true when comparing DBSCAN to k$-$means? You can choose more than $1$

a$.$ Both algorithms are equally sensitive to outliers and noisy data.

b$.$ DBSCAN always outperforms $k-$means due to its flexibility and parameter$-$free nature.

c$.$ Choosing the most appropriate algorithm depends on the specific data characteristics and desired clustering granularity.

d$.$ K$-$means requires pre$-$defining the number of clusters, while DBSCAN automatically discovers them.