HMApriori–An Improved Association Analysis Algorithm and Its Application Research

In order to solve the problem of too many candidate sets and redundancy of the classical Apriori algorithm, an improved algorithm based on Hash function and Mark transaction compression HMApriori (Apriori algorithm based on Hash function and Mark transaction compression) was proposed. The HMApriori algorithm uses the hash function to construct the hash table H2 to reduce the number of C2 candidate sets, and reduces the number of comparisons of conflicts and candidate sets by marking transaction compression, thus improving the efficiency of the algorithm. A performance analysis experiment system was constructed for the field of colleges and universities. On the more than 15,000 pieces of true desensitization data derived from the university educational administration system, the HMApriori algorithm was used to analyze the students' multi- disciplinary results, and the experimental results were compared and analyzed. The results show that the algorithm has a significant improvement in reducing the number of C2 candidate sets and time performance.