An Approach of Latent Semantic Space Partition and Web Document Clustering
Fang Donghao · Zhongwen xinxi xuebao · 2011
This paper applies the LDA model to analyze latent semantics of documents and partition the semantic space into low,middle and high frequency space.The semantics in low frequency space are used to detect outlier web documents.The semantics in middle and high frequency space are devoted to document clustering as features of the documents.The quality of clustering results is improved by a mutual-action mechanism between document clusters and semantics.Compared with related work,this paper not only applies LDA model to represent documents,but also analyzes the semantic distribution in depth and applies the results of analysis to web document clustering.Experiments show that the clustering algorithm of the mutual-action between LDA-based document class and semantic in this paper deserve better effects in document clustering.