Convex Clustering: An Attractive Alternative to Hierarchical Clustering
Fig 1
For clarity, we present three random data points extracted from the three classes in the Iris dataset. Black points denote the original data points X and blue points denote the cluster centers U. At μ = 0, X and U coincide. At intermediate μ values (middle figure), U coalesces towards its cluster center. For sufficiently large μ, U converges to cluster centers (right figure). Note that in this demonstration, only the left two points have non-zero pairwise weights wij. Hence, the two resulting clusters reflect the two graphs defined by the matrix of weights.