k-means clustering or classification? Why is choosing the k in the k-means clustering method based on a feature (take a dead or alive patients scenario as an example, k will be 2) considered clustering rather than classification?
Choosing k=2 because you want to find classes "dead" and "alive" does usually not work. That is a common toy approach that barely works on exercise data such as the Iris data set.

Because k-means is unsupervised, there is no guarantee that for k=2 it will find dead/alive. It may as well find male/female (k=2) or Chuck Norris/everybody else (k=2).