Lloyd's algorithm is a popular iterative method used for quantization and clustering, particularly in the context of k-means clustering. It is often employed to partition a dataset into \( k \) clusters by minimizing the variance within each cluster. Here is a summary of the steps involved in Lloyd's algorithm: 1. **Initialization**: Begin by selecting \( k \) initial cluster centroids. These can be chosen randomly from the dataset or via other methods.
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