Victor Powell and Lewis Lehe provide a fantastic interactive, visual explanation of PCA at http://setosa.io/ev/principal-component-analysis/, this is ideal for readers who are new to the core concepts of PCA or who are not quite getting it.

For a lengthier and more mathematically-involved treatment of PCA, touching on underlying matrix transformations, Jonathon Shlens from Google research provides a clear and thorough explanation at http://arxiv.org/abs/1404.1100.

For a thorough worked example that translates Jonathon's description into clear Python code, consider Sebastian Raschka's demonstration using the Iris dataset at http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html.

Finally, consider the sklearn documentation for more details on arguments to the PCA class at http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html.

For a lively and expert treatment of k-means, including detailed investigations of the conditions that cause it to fail, and potential alternatives in such cases, consider David Robinson's fantastic blog, variance explained at http://varianceexplained.org/r/kmeans-free-lunch/.

A specific discussion of the Elbow method is provided by Rick Gove at https://bl.ocks.org/rpgove/0060ff3b656618e9136b.

Finally, consider sklearn's documentation for another view on unsupervised learning algorithms, including k-means at http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html.

Much of the existing material on Kohonen's SOM is either rather old, very high-level, or formally expressed. A decent alternative to the description in this book is provided by John Bullinaria at http://www.cs.bham.ac.uk/~jxb/NN/l16.pdf.

For readers interested in a deeper understanding of the underlying mathematics, I'd recommend reading the work of Tuevo Kohonen directly. The 2012 edition of self-organising maps is a great place to start.

The concept of multicollinearity, referenced in the chapter, is given a clear explanation for the unfamiliar at https://onlinecourses.science.psu.edu/stat501/node/344.