Estimating the number of clusters with Mean Shift algorithm
Mean Shift is a powerful algorithm used in unsupervised learning. It is a non-parametric algorithm used frequently for clustering. It is non-parametric because it does not make any assumptions about the underlying distributions. This is in contrast to parametric techniques, where we assume that the underlying data follows a standard probability distribution. Mean Shift finds a lot of applications in fields like object tracking and real-time data analysis.
In the Mean Shift algorithm, we consider the whole feature space as a probability density function. We start with the training dataset and assume that they have been sampled from a probability density function. In this framework, the clusters correspond to the local maxima of the underlying distribution. If there are K clusters, then there are K peaks in the underlying data distribution and Mean Shift will identify those peaks.
The goal of Mean Shift is to identify the location...
