The invention discloses an improved association rule report
data mining method based on
mutual exclusion expression, relates to a knowledge discovery and
data mining method in the field of
data science, and solves the problems of
high memory consumption and low efficiency when a traditional association rule
algorithm processes massive data. The method comprises the steps of 1, converting data intotransaction data based on a data threshold range, and obtaining a binary
sparse matrix with grouping labels based on data logic; 2, obtaining a set in which all frequent items are 1, and removing a non-frequent item set to obtain a new grouping result; and 3, performing self-connection
iterative search on the frequent item set to search the frequent item set, and
cutting and iterating the candidate item set until a new frequent item set cannot be generated, thereby obtaining an association
rule mining result. The basic idea of the method is to convert structured data into
transaction data, generate groups based on a
mutual exclusion relationship, and perform
rule mining, thereby reducing the computing memory and improving the computing efficiency. The application scene is wide, and the social and economic values are very high.