基于数据挖掘中文书目自动分类算法

提出一种改进的数据挖掘算法。首先采用ICTCLAS系统进行文本预处理,以词频特征构建词条向量；然后融合词频特征和词频-逆向文件频率特征,构建训练样本集的特征矩阵；接着对该矩阵进行奇异值分解变换,得到语义空间,用于对文本特征向量进行语义空间变换,得到语义向量；最后构建联合支持向量机分类器,实现中文书目所对应的语义向量的自动分类。最后做了大量的仿真实验,实验结果表明,本文方法的分类准确率高于现有方法。

Automatic classification algorithm of Chinese bibliography based on Data Mining

an improved algorithm for data mining is proposed. The first use of ICTCLAS system for text preprocessing, construct the term vector in frequency characteristics; then the fusion frequency characteristics and frequency - inverse document frequency features, construct the characteristic matrix of the training sample set; then the matrix singular value decomposition, get the semantic space for semantic space transform of text feature vector, semantic vector; the construction of combined support vector machine classifier, automatic classification of semantic vector corresponding to the Chinese bibliography. At last, a lot of simulation experiments have been done, and the experimental results show that the classification accuracy of this method is higher than that of the existing methods.