21 lines
861 B
Markdown
21 lines
861 B
Markdown
#rs/class/csb320 #rs/notes
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- Everything so far has been supervised learning (mostly)
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- unsupervised learning (clustering)
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- do not know the labels of data.
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- clustering
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- groups instances based on feature similarity
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- results in group assignments, not target output
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- need to extrapolate meaning from clusters
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- ![[ClusteringBasic.excalidraw]]
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- applications
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- taxonomy of living things
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- clustering documents on topic
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- identify areas with similar land use
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- cluster groups of houses for city planning
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- good clustering: high intra-class similarity, low inter-class similarity
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- centroid
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- mean position of a cluster's instances $$\overline X_{i}=\frac{\sum_{j \in C_{i}}X_{i}}{n_{i}}$$
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- Inertia
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- aerage squared distance of the instances from the centroid $$I_{i}=\frac{\sum_{j \in C_{i}}|\overline X_{j} - X_{i}|^2}{n_{i}}$$
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