摘要
采用目前方法对大学生就业指导资源进行数据挖掘时,由于去噪性能差导致方法存在数据冗余、挖掘效率低和精准度较差的问题,因此提出了基于数据挖掘的大学生就业指导资源挖掘方法。利用H-BIRCH算法对就业指导数据进行聚类处理,结合EMD分解方法和小波去噪方法对不同类别的就业信息数据进行去噪,对去噪后的资源数据进行白化处理,采用图模型提取就业指导资源数据特征,完成就业指导资源挖掘。实验结果表明,该方法可以有效简化数据结构,数据冗余纠错率、数据挖掘效率和数据挖掘准确度较高。
When the current method is used for data mining of college students’employment guidance resources,due to the poor denoising performance,the method has the problems of data redundancy,low mining efficiency and poor accuracy.Therefore,a method based on data mining for college students’employment guidance resources is proposed.H-BIRCH algorithm is used to cluster the employment guidance data,combined with EMD decomposition method and wavelet denoising method to de-noise different types of employment information data,and whiten the denoised resource data.The graph model is used to extract the data features of employment guidance resources and complete the employment guidance resource mining.Experiment results show that the proposed method can effectively simplify the data structure,and has high data redundancy error correction rate,data mining efficiency and data mining accuracy.
作者
马薏雯
MA Yi-wen(Apparel&Art Design College,Xi’an Polytechnic University,Xi’an 710048,China)
出处
《信息技术》
2024年第2期128-131,137,共5页
Information Technology
