Correlation Clustering with Stochastic Labellings
Correlation Clustering with Stochastic Labellings
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具有随机标签的相关聚类
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
M. Pelillo
中科院分区：
文献类型：
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作者：
Nicola Rebagliati;S. R. Bulò;M. Pelillo
相关聚类是找到相关图顶点的清晰划分的问题，以最小化聚类分配中的不一致。在本文中，我们讨论了对原始问题设置的放松，允许将顶点概率分配给标签。通过这样做，可以捕获重叠的簇。我们还表明，已知的优化启发式可以应用于问题表述，但可以自动选择类的数量。此外，我们提出了一种简单的方法来构建从再现内核希尔伯特空间采样的一致性函数的集合，该方法允许应用相关聚类，而无需对成对相关值进行经验估计。
Correlation clustering is the problem of finding a crisp partition of the vertices of a correlation graph in such a way as to minimize the disagreements in the cluster assignments. In this paper, we discuss a relaxation to the original problem setting which allows probabilistic assignments of vertices to labels. By so doing, overlapping clusters can be captured. We also show that a known optimization heuristic can be applied to the problem formulation, but with the automatic selection of the number of classes. Additionally, we propose a simple way of building an ensemble of agreement functions sampled from a reproducing kernel Hilbert space, which allows to apply correlation clustering without the empirical estimation of pairwise correlation values.