EXAMINATION $/$ Start Exam

Topic: Unsupervised Techniques

The k$-$means clustering algorithm works by $($Select one$)$

A$.$ iteratively improving the position of $k$ centroids in the sample space until an optimal placement is found.

B$.$ Starting with one point in the sample space, finding more points in the space within a neighborhood $$ until no more points can be found, and then repeating this process for $k-1$ points.

C$.$ iteratively determining the Gaussian distribution $($via its mean and standard deviation$)$ of $k$ clusters until the probabilities of all points in the sample space are maximized.

D$.$ pairing each point with another point such that their distance is minimized, and then repeating this process with larger groups of points until there are only $k$ clusters remaining.

$90F$

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