The goal of the Associations mining function is to find
items that are consistently associated with each other in a meaningful
way. For example, you can analyze purchase transactions to discover
combinations of goods that are often purchased together. The Associations
mining function answers the question: If certain items are present
in a transaction, what other item or items are likely to be present
in the same transaction?
With Association rules or Sequence rules, you can
also predict the potential revenue if you want to promote an article
to customers who have already bought a particular article. For example,
you might want to know the potential revenue if you offer hiking shoes
to customers who have bought hiking gear.
The relationships discovered by the Associations mining function
are expressed as association rules. In a typical commercial
application, the mining function finds associations and also assigns
probabilities. For example, it can find that, if customers buy paint,
there is a 20% chance that they will also buy a paintbrush. It also
finds multiple associations, for example, if a customer buys paint
and paintbrushes, there is a 40% chance they will also buy paint thinner.
You can use
Intelligent Miner® Visualizer to
analyze the association rules in mining models created, for example,
by
Intelligent
Miner.
When analyzing association rules, you must read the rules and decide
if they are:
- Chance correlations. For example, two items were on sale at half
price on the same day, and therefore were correlated by chance.
- Known correlations. For example, the paint and paintbrush correlation
is something that is already known.
- Unknown but trivial correlations. For example, a correlation between
red gloss paint and red non-gloss paint may be unknown but unimportant.
- Unknown and important correlations. For example, a correlation
between paint and basketballs may be previously unknown. It may also
be very useful in both the organization of advertising and product
placement within the store.
Association rules discovery is used in market basket analysis,
item placement planning, and promotional sales planning, among many
other applications.