Latent Clustering on Graphs with Multiple Edge Types
Latent Clustering on Graphs with Multiple Edge Types
复制标题
具有多种边类型的图上的潜在聚类
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Ali Pinar
中科院分区：
文献类型：
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作者：
M. Rocklin;Ali Pinar
我们研究了具有多个边类型的图的聚类。我们的主要动机是，对象之间的相似性可以用许多不同的度量来衡量，因此允许具有多变量边缘的图显着提高了建模能力。在这种情况下，聚类问题变得更具挑战性。每个边/度量仅提供关于数据的部分信息;恢复全部信息需要聚合所有相似性度量。我们将单边图中的聚类概念推广到多边图中，讨论了多边图如何生成聚类空间，并在此空间上描述了一种元聚类结构，提出了一种元聚类结构的压缩表示方法.给出了在真实的和合成数据上的实验结果。
We study clustering on graphs with multiple edge types. Our main motivation is that similarities between objects can be measured in many different metrics, and so allowing graphs with multivariate edges significantly increases modeling power. In this context the clustering problem becomes more challenging. Each edge/metric provides only partial information about the data; recovering full information requires aggregation of all the similarity metrics. We generalize the concept of clustering in single-edge graphs to multiedged graphs and discuss how this generates a space of clusterings.We describe a metaclustering structure on this space and propose methods to compactly represent the meta-clustering structure. Experimental results on real and synthetic data are presented.