Graph-based data clustering via multiscale community detection
This addresses clustering challenges for data analysts by providing an automated, parameter-robust approach, though it appears incremental as it builds on existing graph and community detection techniques.
The paper tackles the problem of data clustering by introducing a graph-based method using multiscale community detection, which automatically estimates the number of clusters and reduces sensitivity to graph parameters, achieving improved performance over popular clustering methods on synthetic and real datasets.
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.