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![Association Rules Mining(ARM)
• Association rule mining is the data mining process of finding
the rules that may govern associations and causal objects
between sets of items.
• ARM is also called Market Basket Analysis(MBA) & affinity
analysis.
• Set of Items in a transaction is called is called Market Basket.
• Mostly used in RETAIL.
• If ‘A’then ‘B’ {A=>B} [Where A is antecedent & B is consequent ]](https://image.slidesharecdn.com/associationrulesbyarpitsharma-200410100842/85/Association-rules-by-arpit_sharma-2-320.jpg)
Uploaded byEr. Arpit Sharma
Association rules by arpit_sharma
Association rule mining (ARM) is a data mining technique used to discover relationships between variables in large databases. It finds frequent patterns, associations, correlations, or causal structures among sets of items or objects in transactional databases. ARM is commonly used in retail by analyzing customer purchase histories to find relationships between products customers buy together. The Apriori algorithm is commonly used for ARM. It generates candidate item sets and then scans the database to determine which item sets meet the minimum support and confidence thresholds.
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Association rules by arpit_sharma
- 2. Association Rules Mining(ARM) •Association rule mining is the data mining process of finding the rules that may govern associations and causal objects between sets of items. • ARM is also called Market Basket Analysis(MBA) & affinity analysis. • Set of Items in a transaction is called is called Market Basket. • Mostly used in RETAIL. • If ‘A’then ‘B’ {A=>B} [Where A is antecedent & B is consequent ]
- 3.
- 4. Two Major termsin ARM • Support: (S)- Percentage of Transaction (T) that contains both A and B. {A=>B} = P(AꓴB) it measures frequency of association. • Confidence: (C)- In a Transaction Set (T) if C is the % times of times B is in all the transaction Containing A. C=P(B/A)= P(AꓴB)/P(A) ConditionProbability
- 5. Parameters in ARM •Finding all items that appears frequently in transaction.(Minimum Support Count). • Finding Strong Associations among frequent items
- 6.
- 7. Apriori Algorithm Algo: Itis idea to generate Candidate item sets of a given size and then scan dataset to check if their counts are really large. • All item sets are candidate in the first pass,any item with less them specified support value is eliminated. • We create n number of item sets like one,two –n • Generate association rules which have confidence values greater then or equal to specified min confidence.
- 8. Apriori Algorithm Question: Forthe Following transaction given data set ,generates rules using Apriori algorithm .Consider the values as SUPPORT=22% and CONFIDENCE=70% TransactionID Items Purchased 1 I1,I2,I5 2 I2,I4 3 I2,I3 4 I1,I2,I4 5 I1,I3 6 I2,I3 7 I1,I3 8 I1I2,I3,I5 9 I1,I2,I3 Item Frequency Support I1 6 6/9=66% I2 7 7/9=80% I3 6 6/9=66% I4 2 2/9=22.2% I5 2 2/9=22.2% C1 All item support >=22% Minimum Frequency Support
- 9. Apriori Algorithm Now Generatepairs of itemsets C3 Item Set Frequency Support I1,I2 4 4/9=44.4% I1,I3 4 4/9=44.4% I1,I4 1 1/9=11.1% I1,I5 2 2/9=22.2% I2,I3 4 4/9=44.4% I2,I4 2 2/9=22.2% I2,I5 2 2/9=22.2% I3,I4 0 0 I3,I5 1 1/9=11.1% I4,I5 0 0 ItemSet Frequency Support I1,I2,I3 2 2/9=22.2% I1,I2,I5 2 2/9=22.2% Item Set Frequency (I1,I2)I5 2/4=50% (I1,I5)I2 2/2=100% (I2,I5)I1 2/2=100% I1(I2,I5) 2/6=33% I2(I1,I5) 2/7=29% I5(I2,I1) 2/2=100%
- 10.