What are some common clustering techniques in machine learning?

What are some common clustering techniques in machine learning?

Some common clustering techniques include K-Means, Hierarchical Clustering, and DBSCAN. Would you like to know more about any specific one?

Can you explain how K-Means works?

K-Means works by partitioning data into K distinct clusters based on feature similarity. It iteratively assigns data points to the nearest cluster centroid and updates the centroids until convergence.

What are the advantages of using DBSCAN over K-Means?

DBSCAN can find arbitrarily shaped clusters and is robust to outliers, while K-Means assumes spherical clusters and is sensitive to outliers.

How do I choose the right number of clusters for K-Means?

You can use methods like the Elbow Method or Silhouette Score to determine the optimal number of clusters for K-Means.