In k-Means, the parameter k represents the number of clusters. This is called what?

• A. The Kernel Type
• B. The Distance Metric
• C. The Number Of Clusters
• D. The Initialization Method

Answer: (C)

The main idea is that the parameter k in k-Means sets how many groups the data will be partitioned into. This is the number of clusters (the cluster count) you want the algorithm to form, and it determines how many centroids will exist and how the data will be assigned. It’s a pre-set hyperparameter that directly controls the granularity of the clustering.

It’s not the kernel type, which relates to kernel methods in other algorithms; it’s not the distance metric, which is about how you measure distance to centroids (k-Means typically uses Euclidean distance); and it’s not the initialization method, which concerns how you choose the starting centroids.