A Novel Minimum Spanning Tree Clustering Algorithm Based on Density Core

Table 2

Parameter settings of each clustering method in the ten synthetic datasets.

Datasets	Kmeans	DBSCAN	DPC (%)	DCore	SNNDPC	LDP-MST	MST-DC
D1	N = 3	Eps = 0.5; Minpts = 5	dc = 2	r1 = 0.5; r2 = 0.25; R = 0.6; T1 = 30; Tn = 4	K = 5	N = 3	−
D2	N = 5	Eps = 0.8; Minpts = 5	dc = 2	r1 = 0.5; r2 = 0.45; R = 0.5; T1 = 50; Tn = 15	K = 4	N = 5
D3	N = 4	Eps = 10; Minpts = 5	dc = 2	r1 = 14; r2 = 13; R = 15; T1 = 40; Tn = 8	K = 10	N = 4
D4	N = 3	Eps = 20; Minpts = 5	dc = 2	r1 = 20; r2 = 18; R = 20; T1 = 30; Tn = 8	K = 10	N = 3
D5	N = 2	Eps = 0.1; Minpts = 8	dc = 2	r1 = 0.3; r2 = 0.1; R = 0.2; T1 = 20; Tn = 5	K = 5	N = 2
D6	N = 4	Eps = 15; Minpts = 5	dc = 2	r1 = 20; r2 = 18; R = 21; T1 = 10; Tn = 6	K = 10	N = 4
D7	N = 6	Eps = 6; Minpts = 5	dc = 2	r1 = 14; r2 = 13; R = 14; T1 = 40; Tn = 8	K = 5	N = 6
D8	N = 4	Eps = 0.25; Minpts = 4	dc = 2	r1 = 0.4; r2 = 0.35; R = 0.5; T1 = 10; Tn = 5	K = 10	N = 4
D9	N = 6	Eps = 6; Minpts = 5	dc = 2	r1 = 15; r2 = 15; R = 15; T1 = 35; Tn = 15	K = 15	N = 6
D10	N = 7	Eps = 2; Minpts = 10	dc = 2	r1 = 1; r2 = 0.9; R = 2; T1 = 30; Tn = 10	K = 15	N = 7