Which of the following statements characterizes how k-means clustering is different from hierarchical clustering? (Multiple can be chosen)

		unlike hc, k-means requires setting the number of clusters beforehand
		unlike hc, k-means can be applied on data with categorical variables
		unlike hc, the number of iterations the k-means algorithm runs can be different depending on the randonly chosen centroids
		unlike hc, k-means does not require the data to be normalized (scaled such that the mean is 0 and the standard deviation is 1)
		unlike hc, k-means allows the clusters to be visualized with ggplot