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Semi-supervised Agglomerative Hierarchical Clustering Using Clusterwise Tolerance Based Pairwise Constraints

机译:使用基于聚类公差的成对约束的半监督聚集层次聚类

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摘要

Recently, semi-supervised clustering has been remarked and discussed in many researches. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link are frequently used in order to improve clustering results by using prior knowledges or informations. In this paper, we will propose a clusterwise tolerance based pairwise constraint. In addition, we will propose semi-supervised agglomerative hierarchical clustering algorithms with centroid method based on it. Moreover, we will show the effectiveness of proposed method through numerical examples.
机译:最近,在许多研究中都对半监督聚类进行了讨论。在半监督聚类中,为了通过使用先验知识或信息来改善聚类结果,经常使用成对约束(即必须链接和不能链接)。在本文中,我们将提出基于成簇公差的成对约束。另外,我们将基于质心方法提出半监督的聚类层次聚类算法。此外,我们将通过数值示例说明所提出方法的有效性。

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