Learning the Threshold in Hierarchical Agglomerative Clustering

K. Daniels, C. Giraud-Carrier
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引用次数: 23

Abstract

Most partitional clustering algorithms require the number of desired clusters to be set a priori. Not only is this somewhat counter-intuitive, it is also difficult except in the simplest of situations. By contrast, hierarchical clustering may create partitions with varying numbers of clusters. The actual final partition depends on a threshold placed on the similarity measure used. Given a cluster quality metric, one can efficiently discover an appropriate threshold through a form of semi-supervised learning. This paper shows one such solution for complete-link hierarchical agglomerative clustering using the F-measure and a small subset of labeled examples. Empirical evaluation demonstrates promise
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层次聚类阈值的学习
大多数分区聚类算法需要先验地设置所需聚类的数量。这不仅有点违反直觉,而且除了在最简单的情况下,它也很困难。相比之下,分层集群可以创建具有不同数量集群的分区。实际的最终分区取决于所使用的相似性度量的阈值。给定一个聚类质量度量,人们可以通过半监督学习的形式有效地发现一个适当的阈值。本文给出了一种利用f测度和标记样本的小子集的完全链接层次聚集聚类的解决方案。实证评价表明前景看好
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