How do I prepare data for K-means clustering?

How do I prepare data for K-means clustering?

To prepare data for K-means, you should start by normalizing or standardizing your data to ensure all features contribute equally.

What kind of data should I use for K-means?

K-means works best with numerical data. You can use continuous variables or categorical variables that have been encoded.

How many clusters should I choose?

You can use methods like the Elbow Method or Silhouette Score to determine the optimal number of clusters for your data.

Do I need to handle missing values?

Yes, it's important to handle missing values before applying K-means, as they can affect the clustering results.

Can I use K-means for high-dimensional data?

K-means can struggle with high-dimensional data due to the curse of dimensionality, so consider dimensionality reduction techniques.