Question: The k-means clustering algorithm typically requires multiple randomized starting points to hopefully identify the optimal cluster assignments. Briefly explain how this idea relates to optimization

The k-means clustering algorithm typically requires multiple randomized starting points to hopefully identify the optimal cluster assignments. Briefly explain how this idea relates to optimization and why we are not always guaranteed to converge to the optimal solution?

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