Abstract
The density-peak (DP) algorithm is a mode-based clustering method that identifies cluster centers as data points being surrounded by neighbors with lower density and far away from points with higher density. Since its introduction in 2014, DP has reaped considerable success for its favorable properties. A striking advantage is that it does not require data to be embedded in vector spaces, potentially enabling applications to arbitrary data types. In this work, we propose improvements to overcome two main limitations of the original DP approach, i.e., the unstable density estimation and the absence of an automatic procedure for selecting cluster centers. Then, we apply the resulting method to the increasingly important task of graph clustering, here intended as gathering together similar graphs. Potential implications include grouping similar brain networks for ability assessment or disease prevention, as well as clustering different snapshots of the same network evolving over time to identify similar patterns or abrupt changes. We test our method in an empirical analysis whose goal is clustering brain connectomes to distinguish between patients affected by schizophrenia and healthy controls. Results show that, in the specific analysis, our method outperforms many existing competitors for graph clustering.
Chapter PDF
Similar content being viewed by others
Keywords
References
Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., Shir, E.: A model of Internet topology using k-shell decomposition. Proc. Natl. Acad. Sci. 104, 11150–11154 (2007)
Epanechnikov, V.: Non-parametric estimation of a multivariate probability density. Theory Probab. Its Appl. 14, 153–158 (1969)
Ester, M., Kriegel, H., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. KDD-96 34 226–231 (1996)
Gutiérrez-Gómez, L., Delvenne, J.: Unsupervised network embeddings with node identity awareness. Appl. Netw. Sci. 4, 1–21 (2019)
Gutiérrez-Gómez, L., Vohryzek, J., Chiêm, B., Baumann, P., Conus, P., Do Cuenod, K., Hagmann, P., Delvenne, J.: Stable biomarker identification for predicting schizophrenia in the human connectome. NeuroImage Clin. 27 102316 (2020)
Hammond, D., Gur, Y., Johnson, C.: Graph diffusion distance: A difference measure for weighted graphs based on the graph Laplacian exponential kernel. IEEE GlobalSIP 2013, pp. 419–422 (2013)
Kaufmann, L., Rousseeuw, P.: Clustering by means of medoids. Proc. of the Statistical Data Analysis based on the L1 Norm Conference, Neuchatel, Switzerland, pp. 405–416 (1987)
Li, S., Rizzo, M.: K-groups: A generalization of k-means clustering. ArXiv Preprint ArXiv:1711.04359 (2017)
Mehmood, R., Zhang, G., Bie, R., Dawood, H., Ahmad, H.: Clustering by fast search and find of density peaks via heat diffusion. Neurocomputing. 208, 210–217 (2016)
Mukherjee, S., Sarkar, P., Lin, L.: On clustering network-valued data. NIPS2017, pp. 7074–7084 (2017)
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: Learning distributed representations of graphs. ArXiv Preprint arXiv:1707.05005 (2017)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)
Shimada, Y., Hirata, Y., Ikeguchi, T., Aihara, K.: Graph distance for complex networks. Sci. Rep. 6, 1–6 (2016)
Székely, G., Rizzo, M.: The energy of data. Annu. Rev. Stat. Appl. 4, 447–479 (2017)
Wang, X., Xu, Y.: Fast clustering using adaptive density peak detection. Stat. Methods Med. Res. 26, 2800–2811 (2017)
Xie, J., Gao, H., Xie, W., Liu, X., Grant, P.: Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors. Inf. Sci. 354, 19–40 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2023 The Author(s)
About this paper
Cite this paper
Giubilei, R. (2023). Clustering Brain Connectomes Through a Density-Peak Approach. In: Brito, P., Dias, J.G., Lausen, B., Montanari, A., Nugent, R. (eds) Classification and Data Science in the Digital Age. IFCS 2022. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-031-09034-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-09034-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09033-2
Online ISBN: 978-3-031-09034-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)