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Semi-supervised Document Clustering Based on Latent Dirichlet Allocation (LDA) 被引量:2
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作者 秦永彬 李解 +1 位作者 黄瑞章 李晶 《Journal of Donghua University(English Edition)》 EI CAS 2016年第5期685-688,共4页
To discover personalized document structure with the consideration of user preferences,user preferences were captured by limited amount of instance level constraints and given as interested and uninterested key terms.... To discover personalized document structure with the consideration of user preferences,user preferences were captured by limited amount of instance level constraints and given as interested and uninterested key terms.Develop a semi-supervised document clustering approach based on the latent Dirichlet allocation(LDA)model,namely,pLDA,guided by the user provided key terms.Propose a generalized Polya urn(GPU) model to integrate the user preferences to the document clustering process.A Gibbs sampler was investigated to infer the document collection structure.Experiments on real datasets were taken to explore the performance of pLDA.The results demonstrate that the pLDA approach is effective. 展开更多
关键词 supervised clustering document latent Dirichlet instance captured constraints labeled interested
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