What are the differences between clustering techniques?

What are the differences between clustering techniques?

There are several clustering techniques, including K-means, hierarchical clustering, and DBSCAN. Each has its strengths and weaknesses.

Can you explain K-means clustering?

K-means clustering partitions data into K distinct clusters based on feature similarity, minimizing variance within each cluster.

What about hierarchical clustering?

Hierarchical clustering builds a tree of clusters, allowing for a multi-level grouping of data, which can be visualized as a dendrogram.

When should I use DBSCAN?

DBSCAN is great for identifying clusters of varying shapes and sizes, especially when dealing with noise in the data.