For each chapter in Finding Ghosts in Your Data, I’ll include a few resources that I found interesting. This isn’t a bibliography, strictly speaking, as I might not use all of these in the course of writing, but they were at least worth noting.
- Anomaly Detection Principles and Algorithms by Mehrotra, et al. I’ll reference this book frequently, as I think it’s a really good summary of the current state of anomaly detection in academic literature. The first 5-6 chapters are fairly “light” in the sense that an intelligent non-statistician can get a lot of information from them, though as you get deeper into the book, the math starts to pile up.
- Outlier Analysis by Aggarwal. Unlike Mehrotra, et al, this is intended to be a textbook, and Aggarwal writes it as such. I don’t think it makes sense for most developers to read this book unless they’re really interested in the math behind anomaly detection and have enough of a background to make sense of it.
- Properties of Data Sets that Conform to Benford’s Law. I make minor reference to this in chapter 1 and will have a more thorough write-up of the article in an In the Paper coming up soon.