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Association Rules
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The same concept underlies Relationship Mining, Market Basket Analysis, and Affinity Analysis: how are two entities connected to one another and is there any reliance between them.
Probabilistic 'if-then' statements are what association rules are. The following procedures are used to create the statements that exhibit genuine dependency the best.
if the statement's Antecedent is a portion of it.
The next sentence is referred to be Consequent.
Support:
Percentage / Number of transactions in which IF / Antecedent & THEN / Consequent appear in the data
Drawbacks of Support:
Confidence:
Percentage of If/Antecedent transactions that also have the Then/Consequent item set.
P (Consequent | Antecedent) = P(C & A) / P(A)
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Drawbacks of Confidence:
- Carries the same drawback as of Support
- It does not capture the true dependency - How good is the dependency between entities which have high Support?
Lift Ratio is a measure describing the ratio between dependency and independency between entities.
Formula: Confidence / Benchmark Confidence
Note: Benchmark Confidence assumes independence between
Antecedent & Consequent:
Benchmark Confidence:
P(C|A) = P(C & A) / P(A) = P(C) X P(A) /P(A) = P(C)
Threshold - 1:
A rule that is helpful in locating subsequent item sets is one where lift > 1. The aforementioned rule is far superior to choosing random transactions.
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