Транзакциялық дерекқорларда жиі кездесетін жиынтықтарды табу және қатысу ережелерін үйрену алгоритмі

Algorithm for frequent item set mining and association rule learning over transactional databases
Apriori is an algorithm for frequent item set mining and association rule learning over relational 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, while historically significant, suffers from a number of inefficiencies or trade offs, which have spawned other algorithms. Candidate generation generates large numbers of subsets (The algorithm attempts to load up the candidate set, with as many as possible subsets before each scan of the database). Bottom up subset exploration (essentially a breadth first traversal of the subset lattice) finds any maximal subset S only after all of its proper subsets. The algorithm scans the database too many times, which reduces the overall performance. Due to this, the algorithm assumes that the database is permanently in the memory. Also, both the time and space complexity of this algorithm are very high: , thus exponential, where is the horizontal width (the total number of items) present in the database. Later algorithms such as Max Miner try to identify the maximal frequent item sets without enumerating their subsets, and perform "jumps" in the search space rather than a purely bottom up approach.