What is spectral clustering and how does it work?

What is spectral clustering and how does it work?

Spectral clustering is a technique that uses the eigenvalues of a similarity matrix to reduce dimensionality before clustering. It helps in identifying clusters in data that are not necessarily spherical in shape.

Can you explain the steps involved in spectral clustering?

Sure! The steps are: 1) Construct a similarity graph, 2) Compute the Laplacian matrix, 3) Find the eigenvalues and eigenvectors, and 4) Use these to perform clustering, typically with k-means.

What are the advantages of using spectral clustering?

Some advantages include its ability to find non-convex clusters, flexibility in defining similarity, and effectiveness in handling high-dimensional data.

Are there any limitations to spectral clustering?

Yes, it can be computationally expensive for large datasets, and the choice of the similarity measure can significantly affect the results.

In what scenarios is spectral clustering particularly useful?

It's particularly useful in image segmentation, social network analysis, and any scenario where the data has complex structures.