Database Reference
In-Depth Information
5.1 Overview
Figure 5.1 shows the general logic behind association rules. Given a large collection
of transactions (depicted as three stacks of receipts in the figure), in which each
transaction consists of one or more items, association rules go through the items
being purchased to see what items are frequently bought together and to discover a
list of rules that describe the purchasing behavior. The goal with association rules is
to discover interesting relationships among the items. (The relationship occurs too
frequently to be random and is meaningful from a business perspective, which may
or may not be obvious.) The relationships that are interesting depend both on the
business context and the nature of the algorithm being used for the discovery.
Figure 5.1 The general logic behind association rules
Each of the uncovered rules is in the form X Y, meaning that when item X is
observed, item Y is also observed. In this case, the left-hand side (LHS) of the rule is
X, and the right-hand side (RHS) of the rule is Y.
Using association rules, patterns can be discovered from the data that allow the
association rule algorithms to disclose rules of related product purchases. The
uncovered rules are listed on the right side of Figure 5.1 . The first three rules suggest
that when cereal is purchased, 90% of the time milk is purchased also. When bread
is purchased, 40% of the time milk is purchased also. When milk is purchased, 23%
of the time cereal is also purchased.
In the example of a retail store, association rules are used over transactions that
consist of one or more items. In fact, because of their popularity in mining customer
transactions, association rules are sometimes referred to as market basket
analysis . Each transaction can be viewed as the shopping basket of a customer
that contains one or more items. This is also known as an itemset. The term itemset
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