Summary

In this chapter, we learned how to collect ecological data for a variety of data science problems. After a brief introduction to the theory of spectral clustering, we showed how spectral clustering could parse out different animal populations through our Gaboon viper distribution example. Finally, we explored spectral clustering of nearest neighbor networks that can be used in semi-supervised learning pipelines through an ecosystem note data example. In Chapter 6, we'll introduce centrality measurements and use them to find tipping points in stock pricing.