Clustering of cognitive systems using robustness features
This protocol is extracted from research article:
Cognitive chimera states in human brain networks
Sci Adv, Apr 3, 2019; DOI: 10.1126/sciadv.aau8535

In the subject-region robustness parameter space, we grouped cognitive systems into clusters using the k-means algorithm and silhouette analysis. We used k = 3, 4, 5, and 6 and identified the stable clustering that maximizes similarity within clusters and dissimilarity across clusters. One can obtain different clusterings of data based on the k value (number of clusters), and the silhouette value assesses the quality of the clustering. A value close to 1 signifies optimal clustering, meaning that the data points are more distant (defined by the Euclidean distance) from other clusters as compared to their own cluster, while a negative value signifies the opposite. Thus, if ai denotes the average distance of a data point i from the data points in its own cluster and bi denotes the average distance from the data points in other clusters, then the silhouette value is given by Si = (biai)/max(ai, bi). For k = 4, we observed an optimized clustering. The corresponding silhouette plot is shown in fig. S9.

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