Summary

Congratulations on your excellent progress on yet another exciting and important chapter. In this chapter, we learned about the concept of data reduction, its uniqueness, the different types, and saw a few examples of how knowing about the tools and techniques we can use for data reduction can be of significant value in our data analytic projects.

First, we understood the distinction between data redundancy and data reduction and then continued to learn about the overarching categories of data reduction: numerosity data reduction and dimensionality data reduction. For numerosity data reduction, we covered two methods and an example to showcase when and where they could be of value. For dimensionality reduction, we covered two categories: supervised and unsupervised dimension reduction.

Supervised dimension reduction is when we pick and choose the independent attributes for prediction or classification data mining tasks, while unsupervised dimension reduction is when we...