Machine Learning Resources

What is Spectral Clustering?

Spectral clustering is an alternative clustering technique that is rooted in graph theory. It works by constructing an adjacency matrix that connects similar points within a neighborhood defined by something like an epsilon radius or k-neighbors. It then projects the data into a lower dimensional space and applies a traditional clustering technique like K-Means on the projected data. Spectral clustering is particularly well-suited for finding non-convex clusters, such as the case where one cluster is embedded around a ring of data points that form a separate cluster.

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