Clustering, Classifying, and Working with Weka - Clojure Data Analysis - page 250

How it works…

The Apriori algorithm looks for items that are often associated together within a transaction.
This can be used for things such as analyzing shopping patterns. In this case, we're viewing
the constellation of attributes related to each mushroom as a transaction, and we're using the
Apriori algorithm to see which traits are associated with which other traits.

The algorithm attempts to ind the premises that imply a set of consequences. For instance,
white veil colors (the premise) imply a partial veil type with a conidence of 1.0, so whenever
the premise is found, the consequence is also found. A white veil color also implies a free gill
attachment, but the conidence is 99 percent, so we know that these two aren't associated all
of the time.

The abbreviated data dump of the preceding traits isn't particularly legible, so here's the same
information as a table:

Premise Consequence Confidence

veil-color=w veil-type=p 1.0000

gill-attachment=f veil-type=p 1.0000

gill-attachment=f, veil-color=w veil-type=p 1.0000

gill-attachment=f veil-color=w 0.9990

gill-attachment=f, veil-type=p veil-color=w 0.9990

gill-attachment=f veil-type=p, veil-color=w 0.9990

veil-color=w gill-attachment=f 0.9977

veil-type=p, veil-color=w gill-attachment=f 0.9977

veil-color=w gill-attachment=f, veil-type=p 0.9977

veil-type=p veil-color=w 0.9754

From this, we can see that a white veil is associated with a partial veil type, a free gill
attachment is associated with a partial white veil, and so on. If we want more information,
we can request more rules using the rules parameter.

There's more…

The Weka documentation at http://weka.sourceforge.net/doc.dev/weka/
associations/Apriori.html has more information about the Apriori class and
its options

For more about the algorithm itself, see Wikipedia's page on the Apriori algorithm at
http://en.wikipedia.org/wiki/Apriori_algorithm