How do I choose the right number of clusters for my data?
Choosing the right number of clusters can be tricky. Have you considered using methods like the Elbow Method or Silhouette Score?
I've heard of the Elbow Method. Can you explain how it works?
Sure! The Elbow Method involves plotting the explained variance against the number of clusters and looking for an 'elbow' point where the rate of variance drops.
What if the Elbow Method doesn't give a clear answer?
If that's the case, you might want to try the Silhouette Score, which measures how similar an object is to its own cluster compared to other clusters.
Are there any other techniques I should consider?
Yes, you can also explore the Gap Statistic or the Davies-Bouldin Index for additional insights on the optimal number of clusters.
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