This paper proposes a probabilistic association rule algorithm, which estimates the probability of any data item set appearing in the transaction database by using a probabilistic method to obtain candidate frequent item sets, and gives the relevant algorithm description and algorithm implementation. The size of the candidate item set and the number of database scans generated by this algorithm are compared with those of the Apriori algorithm, which greatly reduces the number of database scans. Finally, this paper discusses how to apply the probabilistic association rule algorithm to the circulation mining of university library books to achieve the purpose of optimizing the library collection structure.
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