Research And Application Of Clustering Algorithm For Multi-view Text

With the rapid development of the Internet,the representation of document has gradually developed from a single view dimension to multi-view dimension.Typical multi-view document representation include traditional text content view,text behavior view,text environment view,and so on.Multi-view document clustering aims to jointly use the information of multiple views and improve the limitations of single-view information in document clustering,so it has gradually attracted much attention.However,multi-view document has problems such as high-dimensional sparsity,difference and inconsistency,which bring additional difficulties to multi-view clustering.In response to the above problems,this paper proposes a multi-view document clustering model with enhanced semantic embedding(MDCE)and a generative multi-view clustering model based on topic alignment(GMC).Finally,a clustering analysis tool is integrated.In view of the high-dimensional sparsity and view differences,MDCE first designed a deep enchanced semantic mapping for document views,mapping the views to the low-dimensional semantic expression space,and learning the associated semantic complementation from views to obtain a high-quality semantic representation of the view.Secondly,MDCE uses the deep clustering to further optimize the semantic mapping while mining the clustering structure,forming a learning mechanism for deep association semantic mapping and deep multi-view clustering algorithms.This paper conducts experiments on the proposed MDCE model by comparing it with many latest multi-view clustering methods on real datasets.Experimental results show that the performance of the MDCE model is significantly better than other models.Aiming at the view inconsistency of multi-view document representation,the GMC introduces an attention mechanism based on the deep variational generation model VAE to achieve view fusion alignment.The consistency principle of multi-view clustering results is used to supervise multi-view topic learning and cluster assignment at the view level and document level simultaneously.This paper verifies the GMC model on text generation and text clustering tasks,and the experimental results show that the model is effective.Finally,a multi-view text clustering analysis tool is integrated,and these deep multi-view text clustering algorithms are embedded in the tool,so as to fill the vacancy of clustering analysis tools in the field of multi-view text clustering,and provide personalized and diversified services for different users.