Which of the following are advantages of K-means clustering compared to hierarchical clustering?

K-means clustering is favored for several reasons compared to hierarchical clustering, particularly in terms of flexibility and computational efficiency.

One notable advantage is that K-means can accommodate larger datasets due to its less rigid clustering approach. K-means looks for an optimal configuration of clusters by iteratively refining the placement of points, which allows for greater adaptability in the structure of clusters. In contrast, hierarchical clustering involves a more fixed structure that merges or splits clusters based on specific criteria, which can be more restrictive.

Additionally, K-means clustering typically allows for quicker convergence to a solution since it focuses on minimizing the distance between points and their assigned cluster centroids. This makes it less computationally intensive than some hierarchical methods, especially for large datasets.

In summary, the flexibility and adaptability of K-means in forming clusters make it particularly advantageous over hierarchical clustering, aligning well with the rationale behind the correct option.