$\newcommand{\ones}{\mathbf 1}$

# Clustering and k-means

The goal of clustering a set of vectors is to
1. choose the best vectors from the set
Incorrect.
2. divide them into groups of vectors that are near each other
Correct!
3. determine the nearest neighbors of each of the vectors
Incorrect.

Check below all that apply. The $k$-means algorithm

The choice of $k$, the number of clusters to partition a set of vectors into
1. is a personal choice that shouldn't be discussed in public
Incorrect.
2. depends on why you are clustering the vectors
Correct!
3. should always be as large as your computer system can handle
Incorrect.