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.
- 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.
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