The Ward algorithm is a commonly-used hierarchical variance-minimization algorithm of this type.
The Ward algorithm is a commonly-used hierarchical variance-minimization algorithm of this type. An algorithm of this type whose time complexity is quadratic in number of samples and linear in number of iterations sends messages between data points until convergence; that algorithm, which relies on whether a given point can be an exemplar for another point is called affinity propagation. One algorithm of this type relies on defining core samples and a minimum distance between samples in order to isolate high-density regions; that algorithm is called DBSCAN. Another algorithm of this type minimizes a "distortion measure" by reassigning the labels of the data points and recomputing the means, leading it to be known as the "K-means" algorithm for this process. For 10 points, identify this type of algorithm which groups data points together in a multidimensional space based on common features.
The Ward algorithm is a commonly-used hierarchical variance-minimization algorithm of this type.
clustering algorithm