The correct answer is:

c) Both a and b

K-means clustering requires:

• a) Distance metric: To measure the similarity or difference between data points, usually using Euclidean distance.
• b) Number of clusters: You need to predefine the number of clusters (k) before running the algorithm.

Would you like more details on how K-means clustering works or any of its specific components?

Here are 5 related questions:

1. How is the number of clusters determined in K-means clustering?
2. What are the limitations of the K-means clustering algorithm?
3. What role does initialization play in K-means clustering?
4. What is the silhouette score, and how is it used to evaluate clusters?
5. Can K-means clustering handle non-spherical clusters?

Tip: Always normalize your data when using distance-based algorithms like K-means, especially when features have different scales.