Apriori algorithm is a data mining technique used for association rule mining in large datasets. The algorithm is named after the Latin phrase “a priori,” which means “from the earlier.” This algorithm is widely used in market basket analysis to identify the relationship between products in a transactional database.
The Apriori algorithm is based on the concept of frequent itemsets, which refers to the set of items that frequently appear together in a given dataset. The algorithm generates a set of candidate itemsets and then prunes the itemsets that do not meet the minimum support threshold. The support threshold is a user-defined parameter that specifies the minimum frequency required for an itemset to be considered frequent.
The Apriori algorithm has two main steps:
- Generate candidate itemsets:
The algorithm generates a set of candidate itemsets by joining two frequent itemsets. The process is repeated until no new frequent itemsets can be generated. This step is computationally expensive as it involves generating a large number of candidate itemsets.
- Prune infrequent itemsets:
The algorithm prunes the candidate itemsets that do not meet the minimum support threshold. The remaining itemsets are considered frequent itemsets.
The Apriori algorithm has several advantages over other association rule mining algorithms. It is easy to understand and implement, and it works well with large datasets. However, it has some limitations, including:
- The algorithm can be computationally expensive when dealing with a large number of itemsets.
- The algorithm assumes that all items are independent, which is not always true in real-world scenarios.
- The algorithm does not take into account the order in which items appear in a transaction.
In conclusion, the Apriori algorithm is a popular data mining technique used for association rule mining. It generates frequent itemsets by joining two or more itemsets and then pruning the infrequent ones. Although it has some limitations, it is still widely used in market basket analysis and other areas of data mining.