1. 12.1 Affinity Analysis and Market Basket Analysis
  2. 12.2 Support, Confidence, Frequent Itemsets, and the a Priori Property
  3. 12.3 How Does the a Priori Algorithm Work?
  4. 12.4 Extension from Flag Data to General Categorical Data
  5. 12.5 Information-Theoretic Approach: Generalized Rule Induction Method
  6. 12.6 Association Rules are Easy to do Badly
  7. 12.7 How can we Measure the Usefulness of Association Rules?
  8. 12.8 Do Association Rules Represent Supervised or Unsupervised Learning?
  9. 12.9 Local Patterns Versus Global Models
    1. The R Zone
    2. References
    3. Exercises
    4. Hands-On Analysis

12.1 Affinity Analysis and Market Basket Analysis

Affinity analysis is the study of attributes or characteristics that “go together.” Methods for affinity analysis, also known as market basket analysis, seek to uncover associations among these attributes; that is, it seeks to uncover rules for quantifying the relationship between two or more attributes. Association rules take the form “If antecedent, then consequent,” along with a measure of the support and confidence associated with the rule. For example, a particular supermarket may find that of the 1000 customers shopping on a Thursday night, 200 bought diapers, and of the 200 who bought diapers, 50 bought beer. Thus, the association rule would be: “If buy diapers, then buy beer,” with a support of 50/1000 = 5% and a confidence of 50/200 = 25%.

Examples of association tasks in business and research include the following:

  • Investigating the ...

Get Discovering Knowledge in Data: An Introduction to Data Mining, 2nd Edition now with the O’Reilly learning platform.

O’Reilly members experience live online training, plus books, videos, and digital content from nearly 200 publishers.