For clustering analysis we can use PCA to identify principle components that adjust for the greatest degree of variation. For the R function Morpho::procSym, the first PC adjusts for size and subsequent PCs adjust for additional variability in 3D landmarks.

A second approach is to use k-means algorithm to find clusters. One question we have is how to determine the number of clusters or size of ‘k’ for the algorithm. This is based on the following post: https://medium.com/analytics-vidhya/how-to-determine-the-optimal-k-for-k-means-708505d204eb

Determining the number of clusters can be done by the “Elbow Method” or “Silhouette Method”. The Elbow method is more of a decision rule while the Silhouette method is used for validation during clustering with both methods able to be used together to gain confidence in your decision.