高技术通讯（英文版）2005，Vol.11Issue(1)：6-8,3.
Incremental frequent tree-structured pattern mining from semi-structured data
Incremental frequent tree-structured pattern mining from semi-structured data
摘要
Abstract
The paper studies the problem of incremental pattern mining from semi-structrued data. When a new dataset is added into the original dataset, it is difficult for existing pattern mining algorithms to incrementally update the mined results. To solve the problem, an incremental pattern mining algorithm based on the rightmost expansion technique is proposed here to improve the mining performance by utilizing the original mining results and information obtained in the previous mining process. To improve the efficiency, the algorithm adopts a pruning technique by using the frequent pattern expansion forest obtained in mining processes. Comparative experiments with different volume of initial datasets, incremental datasets and different minimum support thresholds demonstrate that the algorithm has a great improvement in the efficiency compared with that of non-incremental pattern mining algorithm.关键词
semi-structured data/labeled ordered tree/tree-structured pattern/incremental pattern miningKey words
semi-structured data/labeled ordered tree/tree-structured pattern/incremental pattern mining分类
信息技术与安全科学引用本文复制引用
Chen Enhong,Lin Le,Wu Gongqing,Wang Shu..Incremental frequent tree-structured pattern mining from semi-structured data[J].高技术通讯（英文版）,2005,11(1):6-8,3.基金项目
Supported by the National Natural Science Foundation of China (No. 60005004) and the Natural Science Foundation of Anhui Province (No. 01042302). （No. 60005004）
