Classifying data with support vector machines

Support vector machines (SVMs) try to divide two groups of data along a plane. An SVM finds the plane that is the farthest from both groups. If a plane comes much closer to group B, it will prefer a plane that is approximately an equal distance from both. SVMs have a number of nice properties. While other clustering or classification algorithms work well with defined clusters of data, SVMs may work fine with data that isn't in well-defined and delineated groupings. They are also not affected by the local minima. Algorithms such as K-Means or SOMs—which begin from a random starting point—can get caught in solutions that aren't bad for the area around the solution, but aren't the best for the entire space. This isn't a problem for SVMs.

Getting ready

First, we'll need these dependencies in our project.clj file:

(defproject d-mining "0.1.0-SNAPSHOT"
  :dependencies [[org.clojure/clojure "1.6.0...