What are the differences between K-means and Hierarchical clustering?

What are the differences between K-means and Hierarchical clustering?

K-means clustering partitions data into K distinct clusters, while Hierarchical clustering creates a tree of clusters.

Which one is better for large datasets?

K-means is generally faster and more efficient for large datasets, while Hierarchical can be computationally expensive.

Can you explain how K-means works?

Sure! K-means works by initializing K centroids, assigning data points to the nearest centroid, and then updating the centroids.

What about the advantages of Hierarchical clustering?

Hierarchical clustering provides a dendrogram that shows the relationships between clusters, which can be very informative.