Building a Mean Shift clustering model
The Mean Shift is a powerful unsupervised learning algorithm that's used to cluster datapoints. It considers the distribution of datapoints as a probability-density function and tries to find the modes in the feature space. These modes are basically points corresponding to local maxima. The main advantage of Mean Shift algorithm is that we are not required to know the number of clusters beforehand.
Let's say that we have a set of input points, and we are trying to find clusters in them without knowing how many clusters we are looking for. Mean Shift algorithm considers these points to be sampled from a probability density function. If there are clusters in the datapoints, then they correspond to the peaks of that probability-density function. The algorithm starts from random points and iteratively converges toward these peaks. You can learn more about it at http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/TUZEL1/MeanShift.pdf.
How to do it…
The...
