<ul data-eligibleForWebStory="true"><li>Researchers revisit randomized seeding techniques for k-means clustering and k-GMM, introducing new families of initialization methods.</li><li>Experiments demonstrate constant factor improvements over traditional methods in terms of final metrics with modest overhead.</li><li>Significant insights are gained into properties of k-means algorithms, such as correlation observations and variance reduction phenomena.</li><li>The newly proposed seeding methods have the potential to become standard practices and open avenues for theoretical analysis.</li></ul>

Improved seeding strategies for k-means and k-GMM

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