Learning Data Mining with Python

Recommending Movies Using Affinity Analysis
The result is much more readable (there are still some issues, but we can ignore them
for now):
Rule #1
Rule: If a person recommends Shawshank Redemption, The (1994), Pulp
Fiction (1994), Silence of the Lambs, The (1991), Star Wars (1977),
Twelve Monkeys (1995) they will also recommend Raiders of the Lost Ark
(1981)
- Confidence: 1.000
Rule #2
Rule: If a person recommends Silence of the Lambs, The (1991), Fargo
(1996), Empire Strikes Back, The (1980), Fugitive, The (1993), Star
Wars (1977), Pulp Fiction (1994) they will also recommend Twelve
Monkeys (1995)
- Confidence: 1.000
Rule #3
Rule: If a person recommends Silence of the Lambs, The (1991), Empire
Strikes Back, The (1980), Return of the Jedi (1983), Raiders of the
Lost Ark (1981), Twelve Monkeys (1995) they will also recommend Star
Wars (1977)
- Confidence: 1.000
Rule #4
Rule: If a person recommends Shawshank Redemption, The (1994), Silence
of the Lambs, The (1991), Fargo (1996), Twelve Monkeys (1995), Empire
Strikes Back, The (1980), Star Wars (1977) they will also recommend
Raiders of the Lost Ark (1981)
- Confidence: 1.000
Rule #5
Rule: If a person recommends Shawshank Redemption, The (1994), Toy
Story (1995), Twelve Monkeys (1995), Empire Strikes Back, The (1980),
Fugitive, The (1993), Star Wars (1977) they will also recommend Return
of the Jedi (1983)
- Confidence: 1.000
Evaluation
In a broad sense, we can evaluate the association rules using the same concept as for
classification. We use a test set of data that was not used for training, and evaluate
our discovered rules based on their performance in this test set.
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