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[Machine Learning] Association Rule Learning - Apriori | Eclat Algorithm 본문
[Machine Learning] Association Rule Learning - Apriori | Eclat Algorithm
paka_corn 2023. 6. 19. 09:31[ What is Association Rule Learning? ]
Association Rule Learning : Association Rule Learning is a data mining technique that discovers rules indicating the co-occurrence of two or more items.
=> Identifiy the relationships between items and discovers valuable rules indicating their co-occurrence.
=> For example, People who bought 'this stuff', they also bought 'this stuff'.
| "You may also like” where you commonly saw in recommendation platforms
[ Apriori ]
Apriori: Apriori is a powerful data mining technique in Association Rule Learning, particularly for discovering associations in large datasets.
( Data Mining : the process of extracting useful information and patterns from large-scale datasets )
- The Apriori algorithm calculates the frequency of occurrence for items in the dataset and generates sets of items with high associations based on their frequencies.
- This allows us to identify the tendency of certain items occurring together when a specific item is present.
- Apriori Algorithm has 3 parts :
(1) Support
(2) Confidence
(3) Lift : confidence divided by support
How It Works?
Step 1)
Set a minimum support and conficdence
Step 2)
Take all the subsets in transcations having higher support than minimum support
Step 3)
Take all the rules of these subsets having higher confidence than minimum confidence
Step 4)
Sort the rules by decreasing lift
=> generally represent the process : Step 1 to 3
[ Eclat ]
Eclat : Eclat algorithm is a data mining algorithm used to find frequent itemsets in a dataset.
- A frequent itemset refers to a collection of items that frequently co-occur in the dataset.
- Eclat Algorithm only has 1 part : Support
=> At least 2-item in a set ! | find the most strongest set of items
=> simplified version of Apriori algorithm
How It Works?
Step 1)
Set a minimum support
Step 2)
Take all the subsets in transcations having higher support than minimum support
Step 3)
Sort these subsets by decreasing support
