期刊
APPLIED NETWORK SCIENCE
卷 5, 期 1, 页码 -出版社
SPRINGERNATURE
DOI: 10.1007/s41109-019-0248-7
关键词
Multiscale community detection; Clustering; Graph partitioning; Data mining
资金
- European Commission [European Union] [607466]
- Engineering and Physical Sciences Research Council (EPSRC) [EP/N014529/1]
- EPSRC [EP/N014529/1] Funding Source: UKRI
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.
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