Artificial intelligence · University
Clustering, distances and interpretation · revision notes
Clustering groups examples according to a representation and similarity rule. The groups are not automatically true natural categories.
Key ideas and conditions
Unsupervised task
Clustering groups examples according to a representation and similarity rule. The groups are not automatically true natural categories.
K-means
K-means alternates assignments to nearby centres with mean updates and seeks to reduce within-cluster squared distances.
Scaling
Feature units can dominate distance. Standardization choices can change clusters and should be justified.
Validation
Inspect stability, domain usefulness and sensitivity to settings. A visually separated plot is not by itself a universal quality certificate.
Explain without looking
- Explain why different initial centres may yield different k-means solutions.
- Which is a reasonable robustness check?
Further reading and review references
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