Abstract：The potential complementation between different languages is ignored while traditional clustering algorithms discover the hidden structures in document collection. Thus, the latent information in the collection can not be reflected by the obtained patterns. Aiming at this problem, multilingual document clustering algorithm based on parallel information bottleneck(ML-IB) is proposed. Firstly, the relevant variables of multiple language information are constructed according to the bag-of-words model. Then,the multiple relevant variables are incorporated into the parallel information bottleneck, and the relevant information between data patterns and multiple relevant variables is preserved maximally. Finally, to optimize the objective function of ML-IB, a draw and merge method based on information theory is proposed to guarantee the convergence of ML-IB to a local optimal solution. Extensive experimental results on multilingual document datasets show that the proposed algorithm significantly outperform the state-of-the-art single and multilingual clustering methods.
闫小强，卢耀恩，娄铮铮，叶阳东. 基于并行信息瓶颈的多语种文本聚类算法*[J]. 模式识别与人工智能, 2017, 30(6): 559-568.
YAN Xiaoqiang, LU Yaoen, LOU Zhengzheng, YE Yangdong. Multilingual Documents Clustering Algorithm Based on Parallel Information Bottleneck. , 2017, 30(6): 559-568.
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