【聚类论文笔记】Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions
2017-05-01 01:14
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Abstract
将多个聚类(划分)结果组合成一个具有更高精确度和鲁棒性的聚类结果。这篇论文其实比多视图聚类更具通用性,它这里“组合”的可以是不同的聚类算法运行之后的聚类结果,这一点与多视图聚类有很大的不同。This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that deter- mined these partitionings.
Introduction
ϕ(i) :第 i 个聚类结果
λ(i) :第 i 个聚类结果所表示的类标指示向量,指出了每个点的类标
Γ:是一个函数(consensus function),整合 r 个聚类结果的意见,然后得到一个一致的聚类结果(consensus labeling λ)
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