Spectral Clustering – How Math is Redefining Decision Making

This involves grouping different data points (customers, products, movies, etc.) Hierarchical clustering is based around organizing data points into a set of similar clusters, then recursively grouping clusters together until you are left with a single cluster. Because the algorithm has to run through every data point and compare groups of data points to other groups of data points, the run time increases dramatically. Usually the algorithm progresses by randomly assigning data points as centroids, followed by assigning data points to the appropriate clusters.