Answer:
d. all of the above
Step-by-step explanation:
K-means clustering is a term used to describe a technique of vector quantization, that attempts to partition "n" observations into "k" clusters whereby each observation refers to the cluster with the closest mean which represents a model of the cluster.
This implies that the K-means algorithm recognizes the "k" number of centroids, and then assigns every data point to the closest cluster while the centroids remain as small as possible.
Hence, the following is required of K-means clustering:
1. defined distance metric
2. number of clusters
3. initial guess of centroids
Therefore, in this case, the correct answer is option D: All of the above.
