Clustering with DBSCAN simply Explained
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Unsupervised Learning53%
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DBSCAN works like this. It picks a point and checks how many points are close to it within a fixed distance. If enough points are nearby, it starts a cluster and adds those points. Then it keeps expanding the cluster by checking each added point and including their nearby points, too. After finishing this cluster, it moves to another unvisited point. If that new point also has enough nearby points, it starts a new cluster and expands it in the same way. Points that never have enough nearby points don't form clusters and remain noise.
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Weโre back with another short from our Artificial Intelligence series! ๐ค
In each video, we simplify complex ideas into clear, meaningful explanations that help you truly understand how AI works. Itโs all about learning in small, powerful steps that connect together to form the bigger picture of Artificial Intelligence.
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