Mini-Batch K-Means

Clustering DS practice problem on Onlearn.

Difficulty: medium.

Topics: Understanding Mini-Batch K-Means Optimization, Mini-batch size selection, Update step for centroids, Random state reproducibility, Assignment step complexity, Memory efficiency in clustering, Unsupervised Learning, Clustering Algorithms, Optimization Techniques, Computational Complexity, Iterative Refinement, Centroid Initialization, Batch Processing, Stochastic Gradient Descent, Convergence Criteria, Inertia Calculation.