About Apriori
Learn how to find associations involving rare events in a large number of items using Apriori.
An association machine learning problem can be decomposed into the following subproblems:
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Find all combinations of items in a set of transactions that occur with a specified minimum frequency. These combinations are called frequent itemsets.
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Calculate rules that express the probable co-occurrence of items within frequent itemsets.
Apriori calculates the probability of an item being present in a frequent itemset, given that another item or items is present.
Association rule machine learning is not recommended for finding associations involving rare events in problem domains with a large number of items. Apriori discovers patterns with frequencies above the minimum support threshold. Therefore, to find associations involving rare events, the algorithm must run with very low minimum support values. However, doing so potentially explodes the number of enumerated itemsets, especially in cases with a large number of items. This increases the execution time significantly. Classification or anomaly detection is more suitable for discovering rare events when the data has a high number of attributes.