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Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining by Glenn J. Myatt

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6.3 ASSOCIATIVE RULES

6.3.1 Overview

The associative rules method is an example of an unsupervised grouping method, that is, the goal is not used to direct how the grouping is generated. This method groups observations and attempts to understand links or associations between different attributes of the group. Associative rules have been applied in many situations, such as data mining retail transactions. This method generates rules from the groups, as the following example:

IF the customer's age is 18 AND

the customer buys paper AND

the customer buys a hole punch

THEN the customer buys a binder

The rule states that 18-year-old customers who purchase paper and a hole punch will often buy a binder at the same time. This rule would have been generated directly from a data set. Using this information the retailer may decide, for example, to create a package of products for college students.

Associative rules have a number of advantages:

  • Easy to interpret: The results are presented in the form of a rule that is easily understood.
  • Actionable: It is possible to perform some sort of action based on the rule. For example, the rule in the previous example allowed the retailer to market this combination of items differently.
  • Large data sets: It is possible to use this technique with large numbers of observations.

There are three primary limitations to this method:

  • Only categorical variables: The method forces you to either restrict your analysis to variables that are categorical or to ...

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