基于密度传播与聚类合并的自动聚类方法
Clustering Based on Density Propagation and Subcluster Merging
- 通过密度传播机制实现节点聚类，无需计算所有节点间距离
- 提出CluCut度量指导聚类合并，可自适应终止合并过程
- 适用于数据空间与图空间，能自动确定最优聚类数
我们提出DPSM方法，一种基于密度的节点聚类技术，能够自动确定聚类数量，并适用于数据空间和图空间。与传统密度聚类方法不同，该方法通过密度传播过程而非计算任意两节点间距离来确定密度，因此更适合图空间。在DPSM中，节点根据传播后的密度被划分为小簇，该划分方法已被证明是正确且完备的。随后，我们将谱聚类思想从单个节点扩展到这些小簇，并引入CluCut度量以指导聚类合并。该度量根据不同簇属性进行多种改进，从而提供何时终止合并过程的依据。多项实验验证了DPSM的有效性及结论的准确性。
原文摘要 · Abstract (English)
We propose the DPSM method, a density-based node clustering approach that automatically determines the number of clusters and can be applied in both data space and graph space. Unlike traditional density-based clustering methods, which necessitate calculating the distance between any two nodes, our proposed technique determines density through a propagation process, thereby making it suitable for a graph space. In DPSM, nodes are partitioned into small clusters based on propagated density. The partitioning technique has been proved to be sound and complete. We then extend the concept of spectral clustering from individual nodes to these small clusters, while introducing the CluCut measure to guide cluster merging. This measure is modified in various ways to account for cluster properties, thus provides guidance on when to terminate the merging process. Various experiments have validated the effectiveness of DOSM and the accuracy of these conclusions.
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