BIBA - Association Rules - Introduction to Association Rules
4 important questions on BIBA - Association Rules - Introduction to Association Rules
Where are association rules for and how are they also called?
- Identify item clusters in event-based or transaction-based databases
- Association Rules also called:
- Market basket analysis
- Affinity analysis
Where are association rules used for in the real world?
- Heavily used in retail for learning about items that are purchased together
- Useful in several other fields:
- A medical researcher might want to learn what symptoms appear together
- In law, word combinations that appear too often might indicate plagiarism
- Information can be used to
- Make decisions on store layouts
- Design the upcoming catalog
- Identify customer segments based on buying patterns
How can you find association rules?
- One itemset has many association rules
- Every transaction is one itemset
- → Supports several rules
- Two-stage Process:
- 1. Generation of frequent itemsets
- i.e., Apriory algorithm
- 2. Selecting the strong rules
- i.e., criteria for judging the strength of the rules
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How can you generate rules?
- → Detect candidates for indicating item associations
- Ideal process (check all possible combinations):
- Find all combinations of single items, pairs of items, triplets of items, and so on
- Requires a long computation time (exponential)
- Practical solution:
- Consider only combinations that occur with higher frequency in the transactions, i.e., data set
- Called frequent itemsets
- Criterion for frequent is “support”
The question on the page originate from the summary of the following study material:
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