Mean-Shift Clustering

Mean shift clustering is a sliding-window-based algorithm that attempts to find dense areas of data points.

1. Mean shift is a hill climbing algorithm which involves shifting this kernel iteratively to a higher density region on each step until convergence.
2. At every iteration the sliding window is shifted towards regions of higher density by shifting the center point to the mean of the points within the window.
3. Continue shifting the sliding window according to the mean until there is no direction at which a shift can accommodate more points inside the kernel.

No need to select the number of clusters as mean-shift automatically discovers this. The drawback is that the selection of the window size/radius “r” can be non-trivial.

source: The 5 Clustering Algorithms Data Scientists Need to Know