Data Mining, 4th Edition

Chapter 8

Data transformations

Abstracts

There are many transformations that can make real-world datasets more amenable to the learning algorithms discussed in the rest of the book. We first consider methods for attribute selection, which remove attributes that are not useful for the task at hand. Then we look at discretization methods: algorithms for turning numeric attributes into discrete ones. Next we discuss several techniques for projecting data into a space that is more suitable for learning: well-known methods for dimensionality reduction, including unsupervised approaches such as principal component analysis, independent component analysis, and random projections, as well as supervised approaches such as partial least squares regression ...