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Association Rules and the Apriori Algorithm: A Tutorial


 
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A great and clearly-presented tutorial on the concepts of association rules and the Apriori algorithm, and their roles in market basket analysis.



Apriori Algorithm

The apriori principle can reduce the number of itemsets we need to examine. Put simply, the apriori principle states that

if an itemset is infrequent, then all its supersets must also be infrequent

This means that if {beer} was found to be infrequent, we can expect {beer, pizza} to be equally or even more infrequent. So in consolidating the list of popular itemsets, we need not consider {beer, pizza}, nor any other itemset configuration that contains beer.

Finding itemsets with high support

Using the apriori principle, the number of itemsets that have to be examined can be pruned, and the list of popular itemsets can be obtained in these steps:

Step 0. Start with itemsets containing just a single item, such as {apple} and {pear}.

Step 1. Determine the support for itemsets. Keep the itemsets that meet your minimum support threshold, and remove itemsets that do not.

Step 2. Using the itemsets you have kept from Step 1, generate all the possible itemset configurations.

Step 3. Repeat Steps 1 & 2 until there are no more new itemsets.

This iterative process is illustrated in the animated GIF below:

Association Rules Apriori Tutorial Explanation.gif

Reducing candidate itemsets using the Apriori Algorithm

As seen in the animation, {apple} was determine to have low support, hence it was removed and all other itemset configurations that contain apple need not be considered. This reduced the number of itemsets to consider by more than half.

Note that the support threshold that you pick in Step 1 could be based on formal analysis or past experience. If you discover that sales of items beyond a certain proportion tend to have a significant impact on your profits, you might consider using that proportion as your support threshold.

Finding item rules with high confidence or lift

We have seen how the apriori algorithm can be used to identify itemsets with high support. The same principle can also be used to identify item associations with high confidence or lift. Finding rules with high confidence or lift is less computationally taxing once high-support itemsets have been identified, because confidence and lift values are calculated using support values.

Take for example the task of finding high-confidence rules. If the rule

 

    {beer, chips -> apple}

 

has low confidence, all other rules with the same constituent items and with apple on the right hand side would have low confidence too. Specifically, the rules

 

    {beer -> apple, chips}
    {chips -> apple, beer}

 

would have low confidence as well. As before, lower level candidate item rules can be pruned using the apriori algorithm, so that fewer candidate rules need to be examined.

Limitations

  • Computationally Expensive. Even though the apriori algorithm reduces the number of candidate itemsets to consider, this number could still be huge when store inventories are large or when the support threshold is low. However, an alternative solution would be to reduce the number of comparisons by using advanced data structures, such as hash tables, to sort candidate itemsets more efficiently.
  • Spurious Associations. Analysis of large inventories would involve more itemset configurations, and the support threshold might have to be lowered to detect certain associations. However, lowering the support threshold might also increase the number of spurious associations detected. To ensure that identified associations are generalizable, they could first be distilled from a training dataset, before having their support and confidence assessed in a separate test dataset.

Bio: Annalyn Ng has worked as a data analyst at Disney Research, Cambridge University, and Singapore's military.

Original. Reposted with permission.

For more posts like this, visit: https://annalyzin.wordpress.com.

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