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Fig. 3 | BMC Bioinformatics

Fig. 3

From: ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data

Fig. 3

The results on the simulated dataset. a An elbow plot for the clustering results of ClusterTAD on the simulated dataset. The percentage of within-cluster variance is plotted against the number of clusters. The elbow point is at K = 5. b The Davies-Bouldin index (DBI) for the different clustering algorithms. c The Silhouette Index (SI) for the different clustering algorithms. d The average Intra-Inter difference scores for the TADs extracted by ClusterTAD with different combinations of clustering algorithms and distance metrics: HC-eulcidean, KM-eulidean, HC-pearson, KM-pearson, HC-cityblock, KM-cityblock, and the EM. HC denotes the hierarchical clustering algorithm, KM the K-means algorithm, and EM the expectation maximization algorithm. HC-euclidean represents the combination of the hierarchical clustering algorithm with Euclidean distance metric

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