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the implementation of Apriori Algorithm
Apriori is a classic algorithm for frequent item set
mining and association rule learning over transactional databases. It proceeds
by identifying the frequent individual items in the database and extending them
to larger and larger item sets as long as those item sets appear sufficiently
often in the database. The frequent item sets determined by Apriori can be used
to determine association rules which highlight general trends in the database:
this has applications in domains such as market basket analysis.
Apriori is
designed to operate on databases containing transactions (for example,
collections of items bought by customers, or details of a website
frequentation)
Each
transaction is seen as a set of items (an item set). Given a threshold C, the
Apriori algorithm identifies the item sets which are subsets of at least C
transactions in the database.
The Apriori
algorithm relies on the principle "Every non-empty subset of a larges
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