If you have ever struggled to understand the KMeans clustering algorithm, such as:
How are the data points assigned to centroids?
How are the centroids reassigned?
When does the algorithm coverage, and more?
…then I created the above video using Manim to help you build an intuitive understanding.
It covers all the steps that we typically follow in KMeans.
Do note that the centroid initialized step in the video is based on randomly selecting
k centroids. But this can vary based on your implementation.
👉 Over to you: If you liked this video, let me know if you wish to see more such animations of ML algorithms.
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