Slide 16.9: Association rule mining

Slide 16.8: Data mining techniques
Slide 16.10: An example of association rule mining
Home Print version

Association Rule Mining

Association rule discovery can be used to find unordered correlations between items found in a set of database transactions.

In the context of web mining, association rules refer to sets of pages that are accessed together with a support value exceeding some specified threshold.

The support is the number of visitors having reached the page in a session. For example, association rule discovery using the Apriori algorithm, which employs breadth-first search and uses a hash tree structure to count candidate item sets efficiently, may reveal a correlation between users who visited a page containing electronic products to those who access a page about sporting equipment. Three examples of association rule mining are

A rule might be that 98% of customers that purchase tires and auto accessories also get automotive services done.

A rule is the statement that 90% of transactions that purchase bread and butter also purchase milk.

30% of clients who accessed /company/announcements/special-offer.html, placed an online order in /company/products/product1.

Association rule discovery is used to relate pages that are most often referenced together in a single server session. Two applications include

Aside from being applicable for business and marketing applications, the presence or absence of such rules can help web designers to restructure their web sites.

The association rules may also serve as a heuristic for prefetching documents in order to reduce user-perceived latency when loading a page from a remote site.

◀
Previous

Slide 16.8: Data mining techniques
Slide 16.10: An example of association rule mining
Home Print version

▶
Next

A lawyer e-mailed a client:
“Thought I saw you on the street the other day.
Crossed over to say hello, but it wasn’t you,
so I went back. One tenth of an hour: $30.”