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| 1 | +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. |
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| 3 | +In contrast to K-means clustering there is no need to select the number of clusters as mean-shift automatically discovers this. That’s a massive advantage. The fact that the cluster centers converge towards the points of maximum density is also quite desirable as it is quite intuitive to understand and fits well in a naturally data-driven sense. |
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| 5 | +The drawback is that the selection of the window size/radius "r" can be non-trivial. |
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| 7 | +More on Clustering: https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 |
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