Learning Data Mining with Python

Recommending Movies
Using Affinity Analysis
In this chapter, we will look at affinity analysis that determines when objects occur
frequently together. This is colloquially called market basket analysis, after one of
the use cases of determining when items are purchased together frequently.
In Chapter 3, Predicting Sports Winners with Decision Trees, we looked at an object
as a focus and used features to describe that object. In this chapter, the data has a
different form. We have transactions where the objects of interest (movies, in this
chapter) are used within those transactions in some way. The aim is to discover
when objects occur simultaneously. In this example, we wish to work out when
two movies are recommended by the same reviewers.
The key concepts of this chapter are as follows:
• Affinity analysis
• Feature association mining using the Apriori algorithm
• Movie recommendations
• Sparse data formats
Affinity analysis
Affinity analysis is the task of determining when objects are used in similar
ways. In the previous chapter, we focused on whether the objects themselves
are similar. The data for affinity analysis is often described in the form of a
transaction. Intuitively, this comes from a transaction at a store—determining
when objects are purchased together.
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