We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation of the number of clusters, as well as alleviating the sensitivity to the parameters in graph construction. We use both synthetic and benchmark real datasets to compare and evaluate several graph construction methods and clustering algorithms, and show that multiscale graph-based clustering achieves improved performance compared to popular clustering methods without the need to set externally the number of clusters.

我们提出了一种基于图论的数据聚类方法，该方法将根据数据创建图与Markov稳定性（一种多尺度社区检测框架）相结合。我们展示了该方法的多尺度能力如何允许估计簇的数量，以及如何减轻对图构造中参数的敏感性。我们同时使用合成的和基准的真实数据集来比较和评估几种图形构造方法和聚类算法，并表明与基于流行度的聚类方法相比，基于多尺度图的聚类无需外部设置聚类数即可实现更高的性能。