The main advantage of Hierarchical clustering is that

		it is computationally easy.
		it can be used with a large number of data points.
		you don't have to specify ahead of time the number of clusters you want.
		it easier to understand the clusters.

The clustering process in K-means clustering ends when

		every point is in its own cluster.
		every point is closest to the center of its cluster.
		the total variation within each cluster is maximized.
		the average variation within each cluster is maximized.

In K-means clustering the centroid of a cluster is

		the distance between the two furthest points in the cluster.
		the distance between the two closest points in the cluster.
		the location of the cluster center point or average.
		the total variation in the cluster.

In the first stage of Hierarchical clustering, if you have N data points to cluster, you start with

		N-1 clusters
		N clusters.
		N/2 clusters.
		one cluster.

In K-means clustering, when plotting the total within-cluster variation against the number of clusters, the Elbow is the point after which

		the total within-cluster variation increases as the number of clusters increases.
		the total within-cluster variation decreases less sharply as the number of clusters increases.
		the total within-cluster variation decreases more sharply as the number of clusters increases.
		the total within-cluster variation increases more sharply as the number of clusters increases.

After the cluster analysis you need to profile the clusters, which involves

		understanding who they are (their demographics).
		understanding how they can be reached (their media habits).
		understanding what benefits they are looking for.
		all of the above.

When doing cluster analysis, as the number of clusters increases, the total within-cluster variation

		increases.
		stays the same.
		decreases.
		approaches infinity.