What are some common mistakes in K-means clustering?
An example of an AI chatbot conversation about common mistakes in K-means clustering.
What are some common mistakes in K-means clustering?
Some common mistakes include choosing the wrong number of clusters, not normalizing data, and ignoring outliers.
How do I determine the right number of clusters?
You can use methods like the Elbow Method or Silhouette Score to help determine the optimal number of clusters.
What happens if I don't normalize my data?
If you don't normalize your data, features with larger ranges can disproportionately influence the clustering results.
Can you explain the Elbow Method?
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.
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