Graph clustering plays an important role in data mining. Based on an input data graph, data points are partitioned into clusters. However, most existing methods keep the data graph fixed during the clustering procedure, so they are limited to exploit the implied data manifold and highly dependent on the initial graph construction. Inspired by the recent development on manifold learning, this paper proposes an Adaptive Consistency Propagation (ACP) method for graph clustering. In order to utilize the features captured from different perspectives, we further put forward the Multi-view version of the ACP model (MACP). The main contributions are threefold: (1) the manifold structure of input data is sufficiently exploited by propagating the topological connectivities between data points from near to far; (2) the optimal graph for clustering is learned by taking graph learning as a part of the optimization procedure; and (3) the negotiation among the heterogeneous features is captured by the multi-view clustering model. Extensive experiments on real-world datasets validate the effectiveness of the proposed methods on both single- and multi-view clustering, and show their superior performance over the state-of-the-arts.

中文翻译：

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图聚类的自适应一致性传播方法

图聚类在数据挖掘中起着重要作用。基于输入数据图，数据点被划分为集群。然而，大多数现有方法在聚类过程中保持数据图固定，因此它们仅限于利用隐含的数据流形并且高度依赖于初始图构建。受流形学习最近发展的启发，本文提出了一种用于图聚类的自适应一致性传播（ACP）方法。为了利用从不同角度捕获的特征，我们进一步提出了 ACP 模型（MACP）的多视图版本。主要贡献有三方面：（1）通过从近到远传播数据点之间的拓扑连接性，充分利用了输入数据的流形结构；(2)通过将图学习作为优化过程的一部分来学习聚类的最佳图；(3) 多视图聚类模型捕获异构特征之间的协商。对真实世界数据集的大量实验验证了所提出的方法在单视图和多视图聚类上的有效性，并展示了它们优于最先进技术的性能。