Assessing the quality of a clustering
summary
Compares concrete clusterings (k‑means, Gaussian mixtures, PAM, hierarchical, DBSCAN, spectral) on datasets like Old Faithful and tetra‑gunilla bee genotypes, and measures quality with multiple indices rather than one number. Introduces a P‑separation index and a density index to capture border behaviour and intra‑cluster valleys, shows tradeoffs (one clustering wins on within‑cluster dissimilarity while another wins on separation), demonstrates results with votes (roughly 18 vs 4 and 7 vs 21) and points to the R fpc package and a planned benchmarking repository.
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chapters (5)
- 0:00Clustering quality assessment
- 12:23Benchmarking clustering methods
- 24:54Cluster separation and density
- 33:50Multi-dimensional evaluation
- 36:21Q&A
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