Mining of Massive Datasets

This chapter is not intended to be a complete discussion of machine learning. We concentrate on a small number of ideas, and emphasize how to deal with very large data sets. Especially important is how we exploit parallelism to build models of the data. We consider the classical “perceptron” approach to learning a data classifier, where a hyperplane that separates two classes is sought. Then, we look at more modern techniques involving support-vector machines. Similar to perceptrons, these methods look for hyperplanes that best divide the classes, so that few, if any, members of the training set lie close to the hyperplane. We next consider nearest-neighbor techniques, where data is classified according to the class(es) of their nearest neighbors in some space. We end with a discussion of decision trees, which are branching programs for predicting the class of an example.