How can I improve the results of my K-means clustering?

How can I improve the results of my K-means clustering?

There are several ways to enhance K-means results, such as feature scaling, choosing the right number of clusters, and using different initialization methods.

What do you mean by feature scaling?

Feature scaling involves normalizing or standardizing your data so that each feature contributes equally to the distance calculations.

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 for your data.

What are some initialization methods I can use?

Common initialization methods include random initialization, K-means++, and using a hierarchical clustering approach to set initial centroids.