Database Reference
In-Depth Information
5.4 Applications of Association Rules
The term
market basket analysis
refers to a specific implementation of
association rules mining that many companies use for a variety of purposes,
including these:
• Broad-scale approaches to better merchandising—what products should be
included in or excluded from the inventory each month
• Cross-merchandising between products and high-margin or high-ticket
items
• Physical or logical placement of product within related categories of
products
• Promotional programs—multiple product purchase incentives managed
through a loyalty card program
Besides market basket analysis, association rules are commonly used for
recommender systems [11] and clickstream analysis [12].
Many online service providers such as Amazon and Netflix use recommender
systems. Recommender systems can use association rules to discover related
products or identify customers who have similar interests. For example, association
rules may suggest that those customers who have bought product A have also
bought product B, or those customers who have bought products A, B, and C are
more similar to this customer. These findings provide opportunities for retailers to
cross-sell their products.
Clickstream analysis refers to the analytics on data related to web browsing and
user clicks, which is stored on the client or the server side. Web usage log files
generated on web servers contain huge amounts of information, and association
rules can potentially give useful knowledge to web usage data analysts. For example,
association rules may suggest that website visitors who land on page X click on links
A, B, and C much more often than links D, E, and F. This observation provides
valuable insight on how to better personalize and recommend the content to site
visitors.
The next section shows an example of grocery store transactions and demonstrates
how to use R to perform association rule mining.
