Mean Shift clustering is a sliding-window-based algorithm that attempts to find dense areas of data points.
It is a centroid-based algorithm meaning that the goal is to locate the center points of each group/class, which works by updating candidates for center points to be the mean of the points within the sliding-window. These candidate windows are then filtered in a post-processing stage to eliminate near-duplicates, forming the final set of center points and their corresponding groups
python sample.py
python sample_scratch.py
-> result ->
- http://primo.ai/index.php?title=Mean-Shift_Clustering
- https://pythonprogramming.net/mean-shift-from-scratch-python-machine-learning-tutorial/
- https://towardsdatascience.com/speeding-up-your-code-1-the-example-of-the-mean-shift-clustering-in-poincar%C3%A9-ball-space-d46169bfdfc8
- https://github.com/zziz/mean-shift