Clustering applications are across all fields, including, but not limited to, statistics, computer science, and biology. Spectral clustering has a great advantage over traditional clustering algorithms. Results from spectral clustering outperform the traditional clustering algorithms.
Before diving deep into spectral clustering, first let's understand a few mathematical terms used in this concept. More information on all the steps discussed is available in Ulrike von Luxburg's article in Statistics and Computing from December 2007.
While computing the similarity among the given set of points, our goal is to divide the points into groups such that each group has points that are similar. Points in one group have to be dissimilar from points in any other group. The overall goal is to find an affinity matrix from the given set of data. There are several popular graph methods to transform points p1, p2, p3….pn pairwise similarities, or...