Improvement and Application of Apriori Algorithm Based on Equalization
Libo Xu, Lin Qiao, Feng Zhao, Bo Yang, Qiong Wang, Ping Ding, Lei Li · 2019
In the era of information explosion, data collection and analysis have become more and more important, and association rule mining has become an important research area of the data mining field. As a classic association rule algorithm, Apriori often falls into the problem of frequently scanning the database and generating a large number of candidate sets. Aiming at these problems, an improved Apriori algorithm MD_Apriori based on the idea of equalization is proposed, which adopts the vertical data structure to mine frequent items and then the proposed approach is applied to mining frequent items of massive alarm information. Experimental results show that the proposed algorithm is significantly superior to the current association rule mining algorithms.