minibatchkmeans

Minibatchkmeans is a clustering algorithm that is used to cluster large datasets. It is a variant of the k-means algorithm, which is a popular clustering algorithm that partitions a dataset into k clusters. However, the k-means algorithm can be computationally expensive when dealing with large datasets, as it requires calculating the distances between all data points and cluster centroids at each iteration.

Minibatchkmeans addresses this issue by randomly selecting a subset of the data (a minibatch) at each iteration to update the cluster centroids. This reduces the computational cost of the algorithm, making it more efficient for large datasets. Additionally, minibatchkmeans can converge faster than the standard k-means algorithm due to the use of stochastic gradient descent.

However, the use of minibatches can lead to suboptimal clustering results, as the algorithm may converge to a local minimum rather than the global minimum. To mitigate this issue, minibatchkmeans can be run multiple times with different initializations to improve the chances of finding the global minimum