The sklearn.cluster.MiniBatchKMeans is a Python module that provides a fast and memory-efficient implementation of the K-means clustering algorithm. It is specifically designed for large datasets, as it uses mini-batches to update the cluster centroids iteratively instead of computing them on the entire dataset. This algorithm is suitable for online or real-time clustering tasks, where the underlying dataset is continuously updated. MiniBatchKMeans also allows for parallel processing, which can further speed up the clustering process.
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