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Improving Support Vector Data Description for Document Clustering

机译:改进文档聚类的支持向量数据描述

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Document clustering has received a lot of attention due to its wide application in many fields. To effectively deal with this problem, a new document clustering algorithm is proposed by using marginal fisher analysis (MFA) and improved support vector data description (SVDD) algorithms in this paper. The high-dimensional document data are first mapped into lower-dimensional feature space with MFA, the improved SVDD is then applied to cluster the documents into different classes in the reduced feature space. Experimental results on two document databases demonstrate the effectiveness of the proposed algorithm.
机译:文档聚类由于其在许多领域中的广泛应用而受到了广泛的关注。为了有效地解决这个问题,本文提出了一种新的文档聚类算法,即使用边际费舍尔分析(MFA)和改进的支持向量数据描述(SVDD)算法。首先使用MFA将高维文档数据映射到低维特征空间中,然后应用改进的SVDD将文档聚类到缩减特征空间中的不同类别中。在两个文档数据库上的实验结果证明了该算法的有效性。

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