Clustering Definition: The Input

$1$ point possible $($graded$)$

Remember that classification takes the training set

$S_n=\{(x^{(i)},y^{(i)})|i=1,$dots,$n\}$

and the number of classes as input. $($where $x^{(i)}$ is the feature vector and $y^{(i)}$ is the label$).($In other words, these were given so that we can find a classifier that will best classify the test set into the given number of classes.$)$

Remember in the lecture above that now we are discussing clustering, which has a different setting and a different goal from classification. Which of the following are the inputs $($givens$)$ of clustering? Select all those apply.

Set of feature vectors $S_n=\{x^{(i)}|i=1,$dots,$n\}$

Set of feature vectors and their labels $S_n=\{(x^{(i)},y^{(i)})|i=1,$dots,$n\}$

The number of clusters $K$

The represffitatives of each cluster $z_1,$dots,$z_K$

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