⚡ Key Parameters to Tune in K-Means Clustering

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K-Means is one of the most widely used clustering algorithms in machine learning. Its performance, however, depends heavily on the right choice of parameters. Let’s analyze which parameters should be tuned to optimize clustering quality.

📌 Question Recap

Q: What parameters of the K-Means clustering algorithm should be tuned to optimize model performance?

Options:

  1. The number of clusters (n_clusters)

  2. The initialization method (init)

  3. The maximum number of iterations (max_iter)

  4. The learning rate

✅ Correct Parameters to Tune

1. Number of Clusters (n_clusters)

  • This is the most important parameter.

  • It defines how many clusters the algorithm will try to form.

  • Wrong choice → poor grouping (too many clusters = overfitting, too few = underfitting).

  • Methods like Elbow Method or Silhouette Score help determine the optimal value.

2. Initialization Method (init)

  • Determines how the initial cluster centroids are chosen.

  • Common choices:

    • "k-means++" (default, helps spread out initial centroids → better results).

    • "random" (faster but can lead to poor convergence).

  • Tuning this helps avoid bad local minima.

3. Maximum Iterations (max_iter)

❌ Incorrect Option

4. Learning Rate

🎯 Final Answer

The parameters that should be tuned are:

  • ✅ The number of clusters (n_clusters)

  • ✅ The initialization method (init)

  • ✅ The maximum number of iterations (max_iter)

❌ Learning rate is not relevant to K-Means.

✨ Takeaway

When tuning K-Means:

  • Focus on how many clusters you want and how they’re initialized.

  • Ensure the algorithm has enough iterations to converge.

  • Don’t waste time looking for a learning rate—it doesn’t exist here.

👉 Do you want me to create a side-by-side comparison table (K-Means vs Gradient Descent-based models) to highlight why learning rate applies to one but not the other?

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