Table 3.

Clustering Algorithms' Fit (DBI) and Agreement (Cohen's Kappa).

	Training dataset (n = 320)			Test dataset (n = 106)
Number of clusters	DBI		Kappa	DBI		Kappa
	k-means	SOM		k-means	SOM
3	1.427	1.54	0.037	1.741	1.696	0.900
4	1.792	1.447	0.061	1.444	1.178	0.078
5	0.188**	1.296	0.843	1.098	1.133	0.320**
6	1.448	1.087	0.934	1.057	1.171	0.390
7	1.413	1.023	0.835	1.177	0.920	0.891
8	0.198	1.057	0.753	1.063	1.034	0.894
9	1.099	0.249*	0.959	1.288	0.979	0.831
10	1.442	0.251	0.884	1.288	0.816	0.627

^**Best fitting solution with the training dataset but lower Kappa value with the test dataset, indicating the disagreement between k-means and SOM.

^*Final chosen solution. Bold values indicate potential final clustering solution and are discussed in the text.