What is a characteristic of K-means clustering in terms of iterations?

K-means clustering is an iterative algorithm used to partition data into distinct groups, known as clusters, based on their similarities. One of the key characteristics of K-means is that it does not have a predetermined number of iterations it must follow to complete the clustering process. Instead, the algorithm continues to iterate until a convergence criterion is met, which typically involves either the centroids of the clusters no longer changing significantly or the assignment of data points to clusters stabilizing.

The nature of the convergence in K-means means that the number of iterations can vary widely depending on the initial placement of centroids, the complexity of the data, and its structure. Some datasets might converge relatively quickly, while others may take many iterations to reach a stable configuration. This variability makes the characteristic that it may take an unspecified number of iterations particularly accurate, as practitioners cannot always predict how many iterations will be required for convergence based on initial conditions or dataset characteristics.