The Basic k-Means Clustering Algorithm
The image showcases Python code implementing the K-Means Clustering algorithm. The code defines essential methods for initializing centroids, assigning clusters, updating centroids, calculating inertia, and fitting the model, providing a complete workflow for clustering tasks. This well-structured snippet offers an intuitive look at one of the most widely used clustering algorithms, making it an excellent resource for data analysts and machine learning practitioners.
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