K-means is not deterministic and it also consists of number of iterations.

The correct answer is: True.

K-means is an iterative algorithm, which means that it starts with an initial guess and then repeatedly updates its estimates until it converges to a solution. This means that the results of K-means can vary depending on the initial guess, and it is not guaranteed to find the global optimum.

K-means is also not deterministic, which means that the results can vary depending on the random initialization of

the algorithm. This is because the algorithm starts with a random guess for the cluster centers, and these initial guesses can have a significant impact on the final results.

Despite these limitations, K-means is a popular clustering algorithm because it is simple to implement and can

be used to cluster data sets of any size. It is also relatively robust to noise and outliers.

Here is a brief explanation of each option:

• Option A: K-means is not deterministic. This means that the results of K-means can vary depending on the initial guess, and it is not guaranteed to find the global optimum.
• Option B: K-means consists of number of iterations. This is true because K-means is an iterative algorithm, which means that it starts with an initial guess and then repeatedly updates its estimates until it converges to a solution.