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MST-BASED SEMI-SUPERVISED CLUSTERING USING M-LABELED OBJECTS

机译:基于MST的对象的基于MST的半监督集群

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Most of the existing semi-supervised clustering algorithms depend on pairwise constraints, and they usually use lots of priori knowledge to improve their accuracies. In this paper, we use another semi-supervised method called label propagation to help detect clusters. We propose two new semi-supervised algorithms named K-SSMST and M-SSMST. Both of them aim to discover clusters of diverse density and arbitrary shape. Based on Minimum Spanning Tree's algorithm variant, K-SSMST can automatically find natural clusters in a dataset by using K labeled data objects where K is the number of clusters. M-SSMST can detect new clusters with insufficient semi-supervised information. Our algorithms have been tested on various artificial and UCI datasets. The results demonstrate that the algorithm's accuracy is better than other supervised and semi-supervised approaches
机译:大多数现有的半监督聚类算法都依赖于成对约束,并且它们通常使用大量先验知识来提高其准确性。在本文中,我们使用另一种称为标签传播的半监督方法来帮助检测聚类。我们提出了两种新的半监督算法,分别称为K-SSMST和M-SSMST。他们两个都旨在发现密度和形状任意不同的簇。基于最小生成树的算法变体,K-SSMST可以通过使用K个标记的数据对象(其中K是聚类数)自动在数据集中找到自然聚类。 M-SSMST可以检测到具有不足的半监督信息的新集群。我们的算法已在各种人工和UCI数据集上进行了测试。结果表明,该算法的准确性优于其他监督和半监督方法

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