Clustering analysis is employed in brain dynamic functional connectivity to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined. In this work we examine the use of the Davies-Bouldin clustering validity index via simulation and real data analysis. Currently employed indexes, such as the Silhouette index, do not provide an effective estimation requiring the use of an elbow criterion. All elbow criteria rely on users experience and introduce uncertainty into the estimation. We demonstrate the feasibility of using the Davies-Bouldin index as a method delivering a unique discrete response to provide automated selection of the number of clusters.
|Title of host publication
|2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - Jul 2019
|41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany
Duration: Jul 23 2019 → Jul 27 2019
|Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
|41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
|7/23/19 → 7/27/19
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics