自然语言信息中关联规则数据精准挖掘仿真  

Accurate mining simulation of association rule data in natural language information

作　　者：钟文佳 张世玉 ZHONG Wenjia;ZHANG Shiyu(Guizhou Police College,Guiyang 150001,China)

机构地区：[1]贵州警察学院,贵州贵阳150001

出　　处：《电子设计工程》2025年第6期174-177,182,共5页Electronic Design Engineering

基　　金：2022年贵州省教育厅高等学校科学研究项目(青年项目)(黔教技[2022]288号)。

摘　　要：为实现对语言信息数据的有效搜索,针对自然语言信息中的关联规则数据提出一种精准挖掘算法。利用自然语言信息关联函数,分析语言信息数据的关联特性,并定义关联码源模板,实现自然语言信息中关联规则的编码标准建模。确定关联数据加权频繁项的取值范围,联合数据决策权重参数,求解具体的挖掘深度,完成自然语言信息中关联规则数据精准挖掘算法的设计。仿真实验结果表明,基于上述方法可以利用已定义的关联规则映射条件,实现对数据信息的有效挖掘,能够较好解决自然语言信息决策与搜索准确性受限的问题。To achieve effective search of language information data,a precise mining algorithm is proposed for association rule data in natural language information.Using natural language information correlation functions to analyze the correlation characteristics of language information data,define the association code source template,and implement the coding standard modeling of association rules in natural language information.Determine the range of values for weighted frequent items in associated data,jointly determine the weight parameters of data decision-making,solve the specific mining depth,and complete the design of an accurate mining algorithm for association rule data in natural language information.The simulation experiment results show that based on the above method,the defined association rule mapping conditions can be utilized to effectively mine and process data information,which can effectively solve the problem of limited accuracy in natural language information decision-making and search.

关 键 词：自然语言信息 关联规则 数据挖掘 码源模板 加权频繁项 

分 类 号：TN939[电子电信—信号与信息处理]