A Novel Tagging Augmented LDA Model for Clustering

Abstract

Clustering has become an increasingly important task in the analysis of large documents. Clustering aims to organize these documents, and facilitate better search and knowledge extraction. Most existing clustering methods that use user-generated tags only consider their positive influence for improving automatic clustering performance. The authors argue that not all user-generated tags can provide useful information for clustering. In this article, the authors propose a new solution for clustering, named HRT-LDA (High Representation Tags Latent Dirichlet Allocation), which considers the effects of different tags on clustering performance. For this, the authors perform a tag filtering strategy and a tag appending strategy based on transfer learning, Word2vec, TF-IDF and semantic computing. Extensive experiments on real-world datasets demonstrate that HRT-LDA outperforms the state-of-the-art tagging augmented LDA methods for clustering.