GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering

Table 2

Average performances (in terms of ) over 100 runs by different ensemble clustering methods (the three highest scores of AVE and the three lowest scores of Var in each column are highlighted in bold).

Method	Balance				Iris				Pima
	MAX	MIN	AVE	VAR	MAX	MIN	AVE	VAR	MAX	MIN	AVE	VAR
GMEAEC	0.723	0.621	0.679	0.00133	0.917	0.877	0.881	0.00099	0.833	0.733	0.820	0.00254
CEGA	0.699	0.542	0.622	0.00544	0.937	0.755	0.920	0.00756	0.725	0.633	0.676	0.00375
CSPA	0.711	0.520	0.610	0.00989	0.920	0.794	0.879	0.00482	0.820	0.712	0.787	0.00543
HGPA	0.655	0.578	0.610	0.00067	0.842	0.702	0.815	0.00082	0.830	0.648	0.778	0.01211
MCLA	0.633	0.456	0.594	0.01012	0.830	0.768	0.791	0.00101	0.820	0.662	0.738	0.00378
KCC	0.694	0.377	0.544	0.01982	0.878	0.544	0.742	0.01351	0.735	0.698	0.716	0.00012
Method	Wine				Magic04				Seg
	MAX	MIN	AVE	VAR	MAX	MIN	AVE	VAR	MAX	MIN	AVE	VAR
GMEAEC	0.952	0.878	0.941	0.00134	0.783	0.655	0.731	0.00134	0.751	0.615	0.707	0.00589
CEGA	0.930	0.840	0.920	0.00252	0.712	0.542	0.677	0.00942	0.659	0.421	0.558	0.00983
CSPA	0.723	0.553	0.693	0.00142	0.824	0.554	0.743	0.01564	0.456	0.235	0.373	0.00873
HGPA	0.830	0.662	0.759	0.00756	0.577	0.432	0.520	0.00546	0.658	0.423	0.504	0.01425
MCLA	0.879	0.320	0.776	0.09844	0.654	0.344	0.526	0.02121	0.778	0.684	0.717	0.00178
KCC	0.886	0.226	0.717	0.11254	0.756	0.498	0.624	0.00899	0.755	0.524	0.633	0.00997