3 Easy Steps to Understand and Implement Spectral Clustering in Python

This video explains three simple steps to understand the Spectral Clustering algorithm: 1) forming the adjacency matrix of the similarity graph, 2) eigenvalue decomposition of the normalized adjacency matrix or Laplacian matrix, and 3) applying the KMeans clustering algorithm to the rows of the top eigenvectors. Spectral clustering provides more flexibility compared to KMeans clustering.

#SpectralClustering #LaplacianMatrix #SimilarityMatrix

[omarfaroukzouak]

Great video with clear explanation! Thank you very much!

[sheikhshafayat]

This video helped me for my machine learning exam. Thanks. I hope your channel grows fast. Love from Korea

[sadrahakim]

Thank you for your video. It would be perfect if you could point to the places that you are explaining. When I am watching the video, I can't understand what part of the slide you are talking about.

[fptofej]

why are you taking anisotropic distribution and taking the dot product in the beginning ?

[STTC_MT]

Amazing Work! However, why did you compute SVD of Normalised Adjacency matrix instead of Normalised Laplacian?

[PRS-]

Great video ! Quick question, what approach should we follow if we want to tune the RBF hyperparameter in Step 1 ? Thank you

[prempant]

Do you know what the steps are are if we need to use the Unnormalised Laplacian Matrix so L = K - D, in your case?

[ronitjorvekar]

How do I fit this model to obtain predictions on the test data?

[tobeallright]

great video! can you share all the code?