Open AccessOptimized frequent pattern mining algorithm based on Can Tree
Author(s) -
Ning Yong,
Xuehua Zhao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012042
Subject(s) - computer science , traverse , tree (set theory) , data mining , association rule learning , path (computing) , algorithm , tree structure , incremental decision tree , basis (linear algebra) , decision tree , decision tree learning , mathematics , binary tree , mathematical analysis , geodesy , programming language , geography , geometry
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.