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Figure 1 Within-groups sum of squares vs number of clusters Within-groups sum of squares vs number of clusters to determine the number needed for k-means.

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Presentation on theme: "Figure 1 Within-groups sum of squares vs number of clusters Within-groups sum of squares vs number of clusters to determine the number needed for k-means."— Presentation transcript:

1 Figure 1 Within-groups sum of squares vs number of clusters Within-groups sum of squares vs number of clusters to determine the number needed for k-means cluster analysis. Within-groups sum of squares vs number of clusters Within-groups sum of squares vs number of clusters to determine the number needed for k-means cluster analysis. The cluster algorithm assumes a given number of clusters to determine the grouping of patients. Figure 1 shows 15 separate cluster algorithms with 1–15 clusters assumed and the within-groups sum of squares calculated for each. Optimally, one would like to select a small number of clusters for ease of interpretation but with a small within-groups sum of squares. When there is a bend (like an elbow), it provides evidence that the gain is large (i.e., reducing the sum of squares) in identifying a fixed number of clusters with decreased evidence to increase the number of clusters. Our analysis showed no evidence of a bend, so we chose 2 clusters for convenience of interpretation. Hiroshi Mitsumoto et al. Neurol Genet 2015;1:e3 © 2015 American Academy of Neurology

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