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Module 2: Data Mining Fundamentals and Association Rule Mining

20267m

Explain different types of association rules, including Boolean, quantitative, single-dimensional, multidimensional, positive and negative association rules. Discuss situations in which each type is useful.

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Types of Association Rules and When Each Is Useful

An association rule A => B states that when A occurs, B tends to occur too, measured by support and confidence.

Types of association rules

Type Meaning Example Useful when
Boolean Items are only present or absent computer => antivirus software Market basket analysis with yes/no purchase data
Quantitative Involves numeric attributes, which are discretized into ranges age(30-39) AND income(42K-48K) => buys(HDTV) Data has numeric attributes such as age, salary, quantity
Single-dimensional Only one predicate (dimension) is used buys(X, "milk") => buys(X, "bread") Simple item-to-item patterns in one table of transactions
Multidimensional Two or more dimensions or predicates age(X, "20-29") AND occupation(X, "student") => buys(X, "laptop") Data warehouses and customer data with many attributes
Positive Rule on presence of items (A => B) tea => sugar Finding items to bundle, promote or place together
Negative Rule involving absence: A => NOT B, or NOT A => B buys tea => NOT buys coffee Finding substitutes and competing products, fraud and anomaly cases

Related types (for completeness)

  • Multilevel rules: use concept hierarchies ("milk" => "bread" vs "skim milk" => "wheat bread").
  • Single-level rules: all items at the same abstraction level.

Situations where each helps

  • Boolean / single-dimensional / positive: supermarket layout, cross-selling and recommendations.
  • Quantitative: targeting customers by age or income bands, pricing and credit analysis.
  • Multidimensional: customer segmentation and campaigns using several attributes together.
  • Negative: detecting substitute products (so you do not stock both aggressively) and unusual behaviour.

Evaluating the rules

Strong rules meet minimum support and confidence; lift is used to check that A and B are really related (lift > 1 means positive correlation).

Conclusion

The choice of rule type depends on the data (binary or numeric, one or many attributes) and the business goal.

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