Learning Data Mining with Python

Chapter 4
The classic algorithm for affinity analysis is called the Apriori algorithm. It addresses
the exponential problem of creating sets of items that occur frequently within a
database, called frequent itemsets. Once these frequent itemsets are discovered,
creating association rules is straightforward.
The intuition behind Apriori is both simple and clever. First, we ensure that a rule
has sufficient support within the dataset. Defining a minimum support level is the
key parameter for Apriori. To build a frequent itemset, for an itemset (A, B) to have a
support of at least 30, both A and B must occur at least 30 times in the database. This
property extends to larger sets as well. For an itemset (A, B, C, D) to be considered
frequent, the set (A, B, C) must also be frequent (as must D).
These frequent itemsets can be built up and possible itemsets that are not frequent
(of which there are many) will never be tested. This saves significant time in testing
new rules.
Other example algorithms for affinity analysis include the Eclat and FP-growth
algorithms. There are many improvements to these algorithms in the data mining
literature that further improve the efficiency of the method. In this chapter, we will
focus on the basic Apriori algorithm.
Choosing parameters
To perform association rule mining for affinity analysis, we first use the Apriori
to generate frequent itemsets. Next, we create association rules (for example, if a
person recommended movie X, they would also recommend movie Y) by testing
combinations of premises and conclusions within those frequent itemsets.
For the first stage, the Apriori algorithm needs a value for the minimum support
that an itemset needs to be considered frequent. Any itemsets with less support will
not be considered. Setting this minimum support too low will cause Apriori to test a
larger number of itemsets, slowing the algorithm down. Setting it too high will result
in fewer itemsets being considered frequent.
In the second stage, after the frequent itemsets have been discovered, association
rules are tested based on their confidence. We could choose a minimum confidence
level, a number of rules to return, or simply return all of them and let the user decide
what to do with them.
In this chapter, we will return only rules above a given confidence level. Therefore,
we need to set our minimum confidence level. Setting this too low will result in rules
that have a high support, but are not very accurate. Setting this higher will result in
only more accurate rules being returned, but with fewer rules being discovered.
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