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Apriori Algorithm

Algorithm for frequent itemset mining in data mining.

Apriori method

The Apriori algorithm is a widely used method in data mining designed to discover frequent itemsets and association rules from transactional datasets. It works by generating candidate itemsets iteratively, evaluating their frequency against a minimum support threshold, and eliminating those that fall below it. This process enables efficient identification of meaningful patterns such as correlations between products purchased together.

Etymology

The Apriori algorithm is named after the Latin phrase 'a priori', which means 'from what comes before'.

Explain Like I’m 5

The Apriori algorithm helps find groups of items that people often buy together, like how shoppers frequently purchase diapers and baby wipes.

Fun Facts

It's named after the Latin term 'a priori', meaning 'from what comes before'
It uses a 'bottom-up' approach
It was introduced by Lawrence Phillips, Piatetsky-Shapiro, and Wong in 1993

FAQ

What is it? An algorithm for frequent itemset mining
Who uses it? Data miners and machine learning engineers
What is an itemset? A collection of items

Hypernyms — Broader Categories

data mining machine learning

Holonyms — Wholes or Groups

association rule learning

Indexes

machine learning algorithm

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