Optimized frequent pattern mining algorithm based on Can Tree

• Published in: Journal of physics. Conference series Vol. 1883; no. 1; p. 12042
• Main Authors: Yong, Ning Shi, Xiao, Zhao
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.04.2021
• Subjects: Algorithms; Data mining; Pattern analysis; Physics
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1883/1/012042

Due to the continuous dynamic changes of data in the current era, research on incremental association rules is necessary. Among them, frequent pattern mining has always been the subject of research. The research found that among the existing algorithms, Can Tree is very suitable for incremental mining because of its superior nature that it does not require adjustment, merging, and/or splitting of tree nodes during maintenance. In this paper, a new method of mining Can Tree is proposed to solve the problem of time consuming caused by repeatedly traversing paths when obtaining conditional mode basis. The path only needs to be traversed once to meet the requirements and verify it. Experimental results show that the performance of the algorithm is better than the traditional Can Tree algorithm, reducing time consumption to a certain extent.