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关注：1
2013-05-23 12:21
求翻译：In this paper, we focus on clustering unlabeled sets of feature vectors. To cluster those objects, the common approach so far is to select some distance measures for point sets like [6, 7] and then apply a distance-based clustering algorithm e.g. k-medoid methods like CLARANS [8] or a density-based algorithm like DBSCA是什么意思？ 待解决
 悬赏分：1
- 离问题结束还有
In this paper, we focus on clustering unlabeled sets of feature vectors. To cluster those objects, the common approach so far is to select some distance measures for point sets like [6, 7] and then apply a distance-based clustering algorithm e.g. k-medoid methods like CLARANS [8] or a density-based algorithm like DBSCA
问题补充： |
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2013-05-23 12:21:38
正在翻译，请等待...
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2013-05-23 12:23:18
在这份文件中,我们把重点放在群集未标记的功能设置引导程序。 为群集的对象,共同的办法,是要选择一些距离测量点的设置如[6,7],然后将一个距离的聚类算法如kmedoidclarans方法[8]或密度的dbscan算法等[9]。 但是,这种做法并不产生富有表现力群集模型。
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2013-05-23 12:24:58
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2013-05-23 12:26:38
在本白皮书中，我们着重于聚类的特征向量的标记的集。为群集这些对象，常见的方法至今是要选择一些距离的措施
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2013-05-23 12:28:18
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