Correlation clustering is a central topic in unsupervised learning, with many applications in ML and data mining. In correlation clustering, one receives as input a signed graph and the goal is to partition it to minimize the number of disagreements. In this work we propose a massively parallel computation (MPC) algorithm for this problem that is considerably faster than prior work. In particular, our algorithm uses machines with memory sublinear in the number of nodes in the graph and returns a constant approximation while running only for a constant number of rounds. To the best of our knowledge, our algorithm is the first that can provably approximate a clustering problem on graphs using only a constant number of MPC rounds in the sublinear memory regime. We complement our analysis with an experimental analysis of our techniques.

中文翻译：

---

恒定多轮并行中的相关聚类

相关聚类是无监督学习的核心主题，在 ML 和数据挖掘中有许多应用。在相关聚类中，人们接收一个带符号的图作为输入，目标是对其进行分区以最小化分歧的数量。在这项工作中，我们针对这个问题提出了一种大规模并行计算 (MPC) 算法，该算法比之前的工作快得多。特别是，我们的算法使用的机器的内存在图中的节点数量上是次线性的，并在仅运行固定轮数时返回一个常数近似值。据我们所知，我们的算法是第一个可以证明在亚线性内存机制中仅使用恒定数量的 MPC 轮来逼近图上的聚类问题的算法。我们通过对我们技术的实验分析来补充我们的分析。