We have covered Decision tree, instance and kernel-based supervised and unsupervised learning methods in the previous chapters. We also explored the most commonly used algorithms across these learning algorithms in the previous chapters. In this chapter, we will cover association rule based learning and, in specific, Apriori and FP-Growth algorithms among others. We will learn the basics of this technique and get hands-on implementation guidance using Apache Mahout, R, Julia, Apache Spark, and Python. The following figure depicts different learning models covered in this book. The techniques highlighted in orange will be dealt with in detail in this chapter.
The following topics are covered in depth in this chapter:
Understanding the basics and core principles of association rules based learning models
Core use cases for association rule such as the Market Basket problem
Key terms such as itemsets, lift, support, confidence and frequent itemsets, and...