A LDA-Based Approach for Semi-Supervised Document Clustering
Ruizhang Huang, Ping Zhou, Li Zhang · International Journal of Machine Learning and Computing · 2014
In this paper, we develop an approach for semi-supervised document clustering based on Latent Dirichlet Allocation (LDA), namely LLDA.A small amount of labeled documents are used to indicate user's document grouping preference.A generative model is investigated to jointly model documents and the small amount of document labels.A variational inference algorithm is developed to infer the document collection structure.We explore the performance of our proposed approach on both a synthetic dataset and realistic document datasets.Our experiments indicate that our proposed approach performs well on grouping documents based on different user grouping preferences.The comparison between our proposed approach and state-of-the-art semi-supervised clustering algorithms using labeled instance shows that our approach is effective.