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Association Rules

  • July 15, 2023
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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.

association rules

Probabilistic 'if-then' statements are what association rules are. The following procedures are used to create the statements that exhibit genuine dependency the best.

association rules

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

association rules

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)

association rules

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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

association rules

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)

association rules

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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