In this chapter, the goal was to provide an introduction to how to use R in order to build and test association rule mining (market basket analysis). Market basket analysis tries to understand what items are purchased together or what items occur together, so you can apply the analysis to healthcare, fraud-detection, and even exploring mechanical issues. As such, we learned how to transform raw data into a transactional structure for use in the arules package.
We're now going to shift gears back to supervised learning. In the next chapter, we're going to cover some poorly understood but essential methods in practical machine learning, that is, analyzing time series data and determining causality.
