Randomized K-Means

Randomized K-Means is an enhanced version of the traditional K-Means clustering algorithm. Standard K-Means randomly selects initial centers and iteratively refines them to group data points into clusters. Randomized K-Means introduces randomness in the initialization process or throughout the algorithm to improve results. This approach helps avoid poor local solutions that can occur with standard methods. By incorporating randomness, it often achieves better, more consistent clustering performance, especially with complex data, leading to more meaningful groupings in fewer iterations. It's a practical way to make clustering more robust and reliable.