What is the main objective of “k-means++” initialization in the K-means clustering algorithm?

K-means++ is an iterative algorithm that attempts to improve the initial cluster centroids in the K-means algorithm. It does this by selecting the initial cluster centroids to be points that are far apart from each other. This helps to ensure that the algorithm converges to a good solution more quickly.

The other options are incorrect. Option A is incorrect because K-means++ does not reduce the number of clusters. Option B is incorrect because K-means++ does not add more clusters. Option C is incorrect because K-means++ does not visualize data.