Stochastic Mean-Shift Clustering

Numerous algorithms have been proposed and investigated, among which the k means [1], Spectral clustering [2, 3], DB-SCAN [4], and the well-known Mean-shift (MS) clustering algorithm. MS is an effective non-parametric iterative algorithm [5], which is versatile for clustering, tracking, and smoothing tasks. A well-known and used variant of MS is the blurring mean-shift (BMS) [6]. Both MS and BMS algorithms can be coined "deterministic" iterative procedures aiming to find local maximiz-ers of an objective function, since they do not involve any random selection of points to perform their update rule. Both MS and BMS algorithms have been applied to a variety of domains, and several variations around their original formulation have been proposed: see [7] for BMS with a Gaussian kernel (known as Gaussian blurring mean-shift); for BMS applied to high-dimensional data clustering see [8].