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This document details the final exploratory clustering analysis conducted to determine the optimal clustering level for Biome Shift Analysis. Utilizing R code, a large proximity matrix generated from Random Forests was applied to the PAM clustering algorithm, incorporating samples at a 10′ resolution. The analysis presents preliminary decisions on clustering levels of 5, 10, 15, 20, 25, and 30, accompanied by silhouette plots. Silhouette values are calculated for each point to assess cluster similarity, guiding the selection of an acceptable clustering solution.
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Clustering SolutionsFINAL Exploratory RunFull 10’ Resolution – 41,311 samples Michael A. Lindgren EWHALE Laboratory Institute of Arctic Biology University of Alaska Fairbanks February 11, 2011
About This Run… • This “FINAL” exploratory run, refers to the decision of which clustering level the group will choose for the final Biome Shift Analysis. • I was able to modify the R code to pass a very large proximity matrix created in RandomForests to the PAM clustering algorithm, where all 10’ resolution samples were included. • The clustering levels I am showing for at least the preliminary decision making about the optimal number are 5, 10, 15, 20, 25, & 30. • Also included are silhouette plots for each cluster level.
Silhouette Plots • The silhouette value for each point is a measure of how similar that point is to points in its own cluster compared to points in other clusters, and ranges from -1 to +1. It is defined as: S(i) = (min(b(i,:),2) - a(i)) ./ max(a(i),min(b(i,:))) • where a(i) is the average distance from the ithpoint to the other points in its cluster, and b(i,k) is the average distance from the ith point to points in another cluster k. *From MathWorks website, developers of Matlab. See document I have attached with this Presentation, which discusses the Silhouette Plots as a metric of deciding when an acceptable cluster solution is achieved.
