摘要
为使得用户及时获取所需的供应链信息,帮助其完成高效决策规划目标,基于TF-IDF算法设计了一种供应链信息定向挖掘模型。首先分析用户对供应链信息的兴趣度,从而构建用户取向模型,再利用特征项构成兴趣主体,通过浏览行为推导用户对供应链信息的兴趣度。运用词语段落标注技术,结合数据结构完成信息预处理,再运用四元组表示预处理后的文本集合。引入位置权值和词跨度权值约束项,使用TF-IDF算法获取供应链信息中每个候选关键词综合权重,再采用移动Agent访问分布数据集构建特征多叉树,并明确定向信息源特征属性,从而建立支持向量机下的供应链信息定向挖掘模型。仿真结果表明,上述模型的挖掘精准度和挖掘效率均较高,具有较强的实用性。
In order to enable users to timely obtain the required supply chain information and help them achieve the goal of efficient decision-making and planning, this study designed a supply chain information oriented mining model based on TF-IDF algorithm. Firstly, the user’s interest in supply chain information is analyzed, and then the user orientation model is constructed. Then, the feature item is used to form the interest subject, and the user’s interest in supply chain information is deduced through browsing behavior. Then, the word paragraph annotation technology is used to complete the information preprocessing combined with the data structure, and then the quaternion is used to represent the preprocessed text set. Based on this, the position of weight and span weights constraint item, use the TF-each candidate keywords in the IDF algorithm for supply chain information synthesis weights, and USES mobile Agent access distribution data set to build more tree, and directional information source attributes, establishes the support vector machine(SVM) under directional mining model of supply chain information. The simulation results show that the model has high mining accuracy and efficiency, and has strong practicability.
作者
路健
范增民
刘彩娜
LU Jian;FAN Zeng-min;LIU Cai-na(Huaxin College of Hebei University of Geosciences,Shijiazhuang Hebei 050700,China)
出处
《计算机仿真》
北大核心
2021年第7期153-156,349,共5页
Computer Simulation
关键词
供应链信息
定向挖掘模型
支持向量机
权重计算
Supply chain information
Directional mining model
Support vector machine
Weight calculation
