
Description The topic of this lecture is clustering for graphs, meaning finding sets of “related” vertices in graphs. The challenge is finding good algorithms to optimize cluster quality. Professor Strang reviews some possibilities. Summary Two ways to separate graph nodes into clusters k-means: Choose clusters, choose centroids, choose clusters, … Fiedler vector: Eigenvector of graph Laplacian: \(+-\) signs give 2 clusters Related sections in textbook: IV.6–IV.7 Instructor: Prof. Gilbert Strang