An Adaptive Multiobjective Genetic Algorithm with Fuzzy -Means for Automatic Data Clustering

Table 5

The result of all algorithms on Acc, RI and NMI.

DataSet	ESSC	VKCM-K-LP	VKFCM-K-LP	MOEASSC	MOKCW	NSGA-II-FCM	ADNSGA2-FCM
Iris
Acc	0.839	0.955	0.947	0.900	0.960	0.920	0.927
RI	0.844	0.944	0.934	0.886	0.950	0.906	0.912
NMI	0.716	0.858	0.832	0.778	0.864	0.777	0.790
Wine
Acc	0.955	0.907	0.938	0.956	0.921	0.944	0.955
RI	0.940	0.883	0.919	0.942	0.897	0.925	0.940
NMI	0.847	0.733	0.796	0.854	0.768	0.823	0.847
Newthyroid
Acc	0.893	0.864	0.809	0.888	0.946	0.953	0.954
RI	0.826	0.834	0.737	0.818	0.913	0.922	0.922
NMI	0.620	0.655	0.541	0.603	0.772	0.786	0.787
Vertebral
Acc	0.474	0.510	0.517	0.479	0.506	0.655	0.655
RI	0.633	0.667	0.661	0.623	0.697	0.683	0.685
NMI	0.269	0.350	0.330	0.249	0.431	0.286	0.302
Image
Acc	0.606	0.624	0.623	0.620	0.635	0	0.658
RI	0.862	0.869	0.871	0.863	0.874	0	0.874
NMI	0.624	0.625	0.649	0.628	0.631	0	0.598
Abalone
Acc	0.507	0.512	0.513	0.503	0.512	0.492	0.516
RI	0.612	0.619	0.619	0.610	0.619	0.610	0.625
NMI	0.163	0.162	0.165	0.161	0.163	0.166	0.168