特征加权的CLSVSM

运用空间向量对文本信息进行合理且有效的表示对文本聚类以及检索的结果有较大影响。共现潜在语义向量空间模型（CLSVSM）深度挖掘了文本特征词之间的共现潜在语义信息并且提升了文本聚类的性能。本文在CLSVSM基础上先引入特征词词频信息,再将引入的词频作为权重赋予CLSVSM的共现强度，最终构建特征加权的CLSVSM。特征加权的CLSVSM在中文数据上的聚类效果如下：在F值方面，相比CLSVSM和Word2vec文本模型分别提高将近2.4%、5.2%，在熵值上相比90%CLSVSM_K和Word2vec文本模型分别降低了将近3.1%、9.0%，相比词频CLSVSM和TF-IDF模型在聚类效果上都有所提高。在英文数据上聚类效果也与其他模型相当。特征加权的CLSVSM的稳定性有待提高,受限于关键词词频信息表达完整程度。

Feature Weighted CLSVSM

The rational and effective representation of document information using spatial vectors has a larger impact on text clustering and retrieval results. The Co-occurrence Latent Semantic Vector Space Model (CLSVSM) deeply excavates the co-occurrence latent semantic information between document feature words and improves the performance of document clustering. Based on CLSVSM, this paper first introduces word frequency information, then, the introduced word frequency is used as a weight to assign the co-occurrence strength in CLSVSM, and finally constructs feature weighted CLSVSM. The clustering effect of feature weighted CLSVSM on Chinese data is as follows: compared with CLSVSM and Word2vec text models, the F value is increased respectively by nearly 2.4% and 5.2%; compared with 90%CLSVSM_K and Word2vec text models， the entropy value is reduced respectively by nearly 3.1% and 9.0%; compared with the word frequency CLSVSM and TF-IDF models, the clustering effect is improved. The clustering effect of feature weighted CLSVSM on English data is similar to that of other models. The stability of feature weighted CLSVSM needs to be improved, which is limited by the completeness of keyword frequency information expression.