Table 7.

Summary of results for Simulation II: Mean cluster recovery precision for each method and each level of each design feature: Part 2 – the average of the true number of clusters minus the algorithmically selected number of clusters.

Design feature	Level	LCM (AIC2)	LCM (AIC3)	LCM (BIC)	K-means (CH)	K-median (MRPC)
Sample size	N = 100	0.16	0.52	0.92	0.91	0.72
	N = 200	−0.11	0.18	0.51	1.05	0.61
	N = 400	−0.27	0.00	0.22	1.04	0.50
Number of clusters	K = 2	−0.16	−0.01	0.00	−0.43	−0.07
	K = 3	−0.18	0.02	0.12	0.52	0.12
	K = 4	−0.15	0.13	0.41	1.23	0.48
	K = 5	−0.03	0.36	0.84	1.52	0.95
	K = 6	0.15	0.66	1.38	2.17	1.55
Number of variables	V = 6	0.22	0.51	0.96	0.83	1.01
	V = 9	−0.15	0.20	0.55	1.31	0.53
	V = 12	−0.29	−0.01	0.14	0.88	0.29
Cluster sizes	Equal	−0.10	0.17	0.41	0.89	0.26
	60%	−0.06	0.34	0.78	1.47	1.24
	10%	−0.06	0.20	0.46	0.65	0.32
Error level	5%	−0.11	0.03	0.21	0.13	0.25
	10%	−0.08	0.20	0.51	1.13	0.60
	15%	−0.04	0.47	0.93	1.75	0.98
Overall		−0.07	0.23	0.55	1.00	0.61