Which term describes computing the new cluster center by averaging member points?

Which term describes computing the new cluster center by averaging member points?

• A. Allocation
• B. Initialization
• C. Euclidean distance
• D. Recalculation

Answer: (D)

In this context, updating a cluster’s center after assigning points is done by averaging all the points in that cluster to form the new centroid. This operation is called recalculation, because you’re recomputing the center based on the current members. In k-means, you typically alternate between assigning points to the nearest center and then recalculating the centers as the mean of the assigned points. The other terms don’t describe this update step: initialization is about choosing starting centers before any assignments; allocation refers to distributing resources or making assignments in a broader sense; Euclidean distance is the metric used to decide which points belong to which cluster, not how the centers are updated.

In this context, updating a cluster’s center after assigning points is done by averaging all the points in that cluster to form the new centroid. This operation is called recalculation, because you’re recomputing the center based on the current members. In k-means, you typically alternate between assigning points to the nearest center and then recalculating the centers as the mean of the assigned points. The other terms don’t describe this update step: initialization is about choosing starting centers before any assignments; allocation refers to distributing resources or making assignments in a broader sense; Euclidean distance is the metric used to decide which points belong to which cluster, not how the centers are updated.