Machine Learning, 2nd Edition

CHAPTER 7Association Rules Learning

Among the machine learning methods available, association rules learning is probably the most used. From point-of-sale systems to web page usage mining, this method is employed frequently to examine transactions. It finds out the interesting connections among elements of the data and the sequence (behaviors) that led to some correlated result.

This chapter describes how association rules learning methods work and also goes through an example using Apache Mahout for mining baskets of purchases. This chapter also touches on the myth, the reality, and the legend of using this type of machine learning.

Where Is Association Rules Learning Used?

The retail industry is tripping over itself to give you, the customer, offers on merchandise it thinks you will buy. To do that, though, it needs to know what you've bought previously and what other customers, similar to you, have bought. Brands such as Tesco and Target thrive on basket analysis to see what you've purchased previously. If you think the amount of content that Twitter produces is big, then just think about point-of-sale data; it's another world. Some supermarkets fail to adopt this technology and never look into baskets, much to their competitive disadvantage. If you can analyze baskets and act on the results, then you can see how to increase bottom-line revenue.

Association rules learning isn't only for retail and supermarkets, though. In the field of web analytics, association rules ...