ObsidianVault/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md

#rs/class/csb320 #rs/notes

---

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