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基于词性和中心点改进的文本聚类方法

, PP. 996-1001

Keywords: 文本聚类,k-均值,词性特征,样本平均相似度,孤立点

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Abstract:

针对k-均值算法对初始点敏感、易陷入局部最优的问题,提出一种基于词性和中心点改进的文本聚类方法(STICS)。通过改进文本的语义型表示,优化中心点的选取,并消除孤立点的负面影响,从而获得较好的聚类效果。STICS考虑不同词性特征对文本的贡献,采用加权的向量空间模型来表示文本。对于中心点的选取,首先度量每个样本的样本平均相似度,其次选取样本平均相似度最大的样本作为第一个聚类中心。此外,STICS消除孤立点的负面影响,以此提高聚类效果。实验结果表明文中方法确实具有更好的聚类效果。

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