BIRCH — when to use it?
hardAnswer
- Streaming / very large dataset clustering.
- Builds a CF-Tree (Cluster Feature summaries: N, LS, SS) in a single pass over data — leaves become micro-clusters.
- Then apply hierarchical or k-means clustering on the leaves.
- Memory-efficient, near-linear in n, streaming-friendly.
- Weakness: assumes spherical clusters (built-in radius threshold).
- Standard for terabyte-scale exploratory clustering.
Check yourself — multiple choice
- Same as k-means
- Streaming clustering via CF-Tree of micro-clusters; near-linear in n; assumes spherical clusters
- Random
- Not real
BIRCH: CF-Tree of summaries; streaming / terabyte scale.
#clustering
Practise Unsupervised Learning
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