In order to avoid users having to obtain useful result sets through \"secondary mining\", this paper proposes a new constrained maximum frequent pattern mining algorithm CSMFP-Max. The CSMFP-Max algorithm is based on CFP trees and symmetric matrices. In the mining process, it adopts multiple pruning strategies and combines top-down and bottom-up bidirectional search strategies, which greatly reduces the size of the candidate set and avoids the generation of unnecessary conditional CFP trees. Theoretical analysis and experimental results show that the CSMFP-Max algorithm is an efficient constrained maximum frequent pattern mining algorithm with good time and space efficiency.
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