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HAN 13-ch06-243-278-9780123814791
2011/6/1

244 Chapter 6 Mining Frequent Patterns, Associations, and Correlations 3:20 Page 244 #2

interesting associations and correlations between itemsets in transactional and relational
databases. We begin in Section 6.1.1 by presenting an example of market basket analysis,
the earliest form of frequent pattern mining for association rules. The basic concepts of
mining frequent patterns and associations are given in Section 6.1.2.

6.1.1 Market Basket Analysis: A Motivating Example

Frequent itemset mining leads to the discovery of associations and correlations among
items in large transactional or relational data sets. With massive amounts of data contin-
uously being collected and stored, many industries are becoming interested in mining
such patterns from their databases. The discovery of interesting correlation relation-
ships among huge amounts of business transaction records can help in many busi-
ness decision-making processes such as catalog design, cross-marketing, and customer
shopping behavior analysis.
A typical example of frequent itemset mining is market basket analysis. This process
analyzes customer buying habits by finding associations between the different items that
customers place in their “shopping baskets” (Figure 6.1). The discovery of these associa-
tions can help retailers develop marketing strategies by gaining insight into which items
are frequently purchased together by customers. For instance, if customers are buying
milk, how likely are they to also buy bread (and what kind of bread) on the same trip

Which items are frequently
purchased together by customers?

Shopping Baskets

bread milk bread bread
milk milk
cereal sugar eggs butter
Customer 1 Customer 2 Customer 3

sugar
eggs
Market Analyst
Customer n

Figure 6.1 Market basket analysis.