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.
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