January 2019
Intermediate to advanced
294 pages
6h 43m
English
Content preview from Machine Learning Quick Reference
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Apriori algorithm
Apriori is a classical algorithm that is used to mine frequent itemsets to derive various association rules. It will help set up a retail store in a much better way, which will aid revenue generation.
The anti-monotonicity of the support measure is one of the prime concepts around which Apriori revolves. It assumes the following:
- All subsets of a frequent itemset must be frequent
- Similarly, for any infrequent itemset, all its supersets must be infrequent too
Let's look at an example and explain it:
|
Transaction ID |
Milk |
Butter |
Cereal |
Bread |
Book |
|
t1 |
1 |
1 |
1 |
0 |
0 |
|
t2 |
0 |
1 |
1 |
1 |
0 |
|
t3 |
0 |
0 |
0 |
1 |
1 |
|
t4 |
1 |
1 |
0 |
1 |
0 |
|
t5 |
1 |
1 |
1 |
0 |
1 |
|
t6 |
1 |
1 |
1 |
1 |
1 |
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.