Call Us

Home / Blog / Data Science Digital Book / Association Rules

Association Rules

  • July 15, 2023
  • 4563
  • 20
Author Images

Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

Read More >

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.

Learn the core concepts of Data Science Course video on YouTube:

The next sentence is referred to be Consequent.


Percentage / Number of transactions in which IF / Antecedent & THEN / Consequent appear in the data

association rules

Drawbacks of Support:


Percentage of If/Antecedent transactions that also have the Then/Consequent item set.

P (Consequent | Antecedent) = P(C & A) / P(A)

association rules

Click here to learn Data Science in Hyderabad

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.

Click here to learn Data Science Course, Data Science Course in Hyderabad, Data Science Course in Bangalore

Data Science Placement Success Story

Data Science Training Institutes in Other Locations

Navigate to Address

360DigiTMG - Data Science Course, Data Scientist Course Training in Chennai

D.No: C1, No.3, 3rd Floor, State Highway 49A, 330, Rajiv Gandhi Salai, NJK Avenue, Thoraipakkam, Tamil Nadu 600097


Get Direction: Data Science Course

Make an Enquiry