Shrinkage Clustering: a fast and size-constrained clustering algorithm for biomedical applications

BMC Bioinformatics

Table 5 Performance comparison of ten algorithms on six biological data sets, i.e. TCGA, BCWD, Dyrskjot-2003, Nutt-2003-v1, Nutt-2003-v3 and AIBT

Data	Metric	Shrinkage	Spectral	K-means	Hierarchical	PAM	DBSCAN	Affinity	AGNES	Clusterdp	SymNMF
TCGA	NMI	0.91	0.77	NA	0.83	0.76	NA	NA	0.82	NA	0.78
	Rand	0.97	0.91	NA	0.91	0.77	NA	NA	0.90	NA	0.94
	F1	0.98	0.92	NA	0.92	0.80	NA	NA	0.92	NA	0.95
	K (3)	3	2	NA	2	2	NA	NA	3	NA	2
BCWD	NMI	0.50	0.29	0.46	0.09	0.50	0.20	0.45	0.09	0.20	0.56
	Rand	0.77	0.68	0.75	0.55	0.77	0.64	0.76	0.55	0.53	0.83
	F1	0.80	0.69	0.79	0.69	0.80	0.75	0.79	0.69	0.59	0.85
	K (2)	2	2	2	2	2	2	3	2	2	2
Dyrskjot-2003	NMI	0.45	0.07	0.51	0.12	0.56	0.30	0.42	0.12	0.07	0.58
	Rand	0.78	0.55	0.76	0.42	0.77	0.55	0.72	0.42	0.50	0.83
	F1	0.70	0.36	0.71	0.54	0.66	0.60	0.66	0.54	0.43	0.75
	K (3)	3	3	3	3	3	3	3	3	2	3
Nutt-2003-v1	NMI	0.56	0.45	0.47	0.28	0.34	0.61	0.41	0.11	0.17	0.49
	Rand	0.72	0.73	0.72	0.52	0.68	0.65	0.73	0.35	0.64	0.72
	F1	0.58	0.51	0.51	0.43	0.41	0.62	0.44	0.38	0.34	0.55
	K (4)	4	4	4	4	4	4	5	4	4	4
Nutt-2003-v3	NMI	1.00	0.20	0.75	0.13	0.33	0.13	0.13	0.13	0.29	0.76
	Rand	1.00	0.58	0.91	0.58	0.58	0.58	0.58	0.58	0.55	0.91
	F1	1.00	0.59	0.92	0.71	0.60	0.71	0.71	0.71	0.57	0.91
	K (2)	2	2	2	2	2	2	3	2	2	2
AIBT	NMI	0.56	0.20	0.58	0.17	0.54	0.56	0.53	0.02	0.55	0.55
	Rand	0.79	0.68	0.80	0.37	0.78	0.65	0.76	0.26	0.69	0.79
	F1	0.61	0.39	0.62	0.40	0.59	0.59	0.51	0.40	0.57	0.61
	K (4)	4	4	4	4	4	4	5	4	3	4

Clustering accuracy is assessed via metrics including NMI (Normalized Mutual Information), Rand Index, F1 score and K (the optimal cluster number). The top three performers in each case are highlighted in bold

ISSN: 1471-2105