Mining association rules


I flickr photo by joannapoe shared under a Creative Commons (BY-SA) license

To find such rules, you would have to execute the rule-induction procedure once for every possible combination of attributes, with every possible combination of values, on the right-hand side. That would result in an enormous number of association rules, which would then have to be pruned down on the basis of their coverage (the number of instances that they predict correctly) and their accuracy (the same number expressed as a proportion of the number of instances to which the rule applies). This approach is quite infeasible. Instead, we capitalize on the fact that we are only interested in association rules with high coverage. We ignore, for the moment, the distinction between the left- and right-hand sides of a rule and seek combinations of attribute–value pairs that have a prespecified minimum coverage. These are called item sets: an attribute–value pair is an item.

Association rules

Once all item sets with the required coverage have been generated, the next step is to turn each into a rule, or set of rules, with at least the specified minimum accuracy. Some item sets will produce more than one rule; others will produce none.

Generating rules efficiently

The first stage proceeds by generating all one-item sets with the given
minimum coverage and then using this to generate the two-item sets, three-item sets (third column), and so on.
Each operation involves a pass through the dataset to count the items in each set, and after the pass the surviving item sets are stored in a hash table.  From the one-item sets, candidate two-item sets are generated, and then a pass is made through the dataset, counting the coverage of each two-item set; at the end the candidate sets with less than minimum coverage are removed from the table. The candidate two-item sets are simply all of the one-item sets taken in pairs, because a two-item set cannot have the minimum coverage unless both its constituent one-item sets have minimum coverage, too. This applies in general: a three-item set can only have the minimum coverage if all three of its two-item subsets have minimum coverage as well, and similarly for four-item sets.


Bibliography

Ian H. Witten, Eibe Frank. (1999). Data mining practical machine learning tools and techniques. Elsevier.

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Author: enroblog

Computer science student at ITESM

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