What are some effective clustering techniques in machine learning?

What are some effective clustering techniques in machine learning?

Some popular 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 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 requires specifying the number of clusters and assumes spherical shapes.

Are there any visualization techniques to help understand these clusters?

Yes, techniques like t-SNE and PCA can help visualize high-dimensional data in 2D or 3D, making it easier to see the clusters.