📚 node [[clustering]]
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garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/Clustering.md by @KGBicheno
clustering
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Grouping related examples, particularly during unsupervised learning. Once all the examples are grouped, a human can optionally supply meaning to each cluster.
Many clustering algorithms exist. For example, the k-means algorithm clusters examples based on their proximity to a centroid, as in the following diagram:
A human researcher could then review the clusters and, for example, label cluster 1 as "dwarf trees" and cluster 2 as "full-size trees."
As another example, consider a clustering algorithm based on an example's distance from a center point, illustrated as follows:
📖 stoas
- public document at doc.anagora.org/clustering
- video call at meet.jit.si/clustering
⥅ related node [[2003 03 27 simple clustering with xserve]]
⥅ related node [[k means clustering]]
⥅ related node [[agglomerative_clustering]]
⥅ related node [[centroid based_clustering]]
⥅ related node [[divisive_clustering]]
⥅ related node [[hierarchical_clustering]]
⥅ related node [[topic clustering in org roam graphs]]
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