Abstract：A generalized method is presented for unsupervised text clustering. The relevance of the features to the mixture components is introduced to the mixture model as a set of latent variables. Then the model selection, feature selection and parameter estimation of the mixture model are integrated into one general framework. Experimental results on four large scale document datasets show that the proposed method achieves fine results in model selection, feature selection and clustering performance.
张亮，李敏强. 一种有限混合模型对无监督文本聚类的广义方法*[J]. 模式识别与人工智能, 2007, 20(5): 698-703.
ZHANG Liang, LI Min-Qiang. A Generalized Method for Unsupervised Text Clustering Using Finite Mixture Models. , 2007, 20(5): 698-703.
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