Table S2.

Accuracy of all algorithms on all datasets, measured by NMI

Dataset	$k$-means++	GMM	fuzzy	MS	AC-C	AC-W	N-Cuts	AP	Zell	SEC	LDMGI	GDL	PIC	RCC	RCC-DR
MNIST	0.500	0.405	0.386	0.282	NA	0.679	n/a	0.609	NA	0.469	0.761	NA	NA	0.893	0.827
Coil-100	0.835	0.832	0.828	0.750	0.739	0.876	0.891	0.843	0.965	0.872	0.906	0.965	0.970	0.963	0.963
YTF	0.788	0.779	0.774	0.846	0.680	0.806	0.758	0.783	0.273	0.760	0.532	0.664	0.684	0.850	0.882
YaleB	0.650	0.621	0.140	0.234	0.479	0.788	0.934	0.799	0.913	0.863	0.950	0.931	0.946	0.978	0.976
Reuters	0.536	0.510	0.272	0.000	0.392	0.492	0.545	0.504	0.087	0.498	0.523	0.401	0.057	0.556	0.553
RCV1	0.355	0.338	0.205	0.000	0.108	0.364	0.140	0.355	0.023	0.069	0.382	0.020	0.015	0.138	0.442
Pendigits	0.680	0.695	0.695	0.703	0.526	0.729	0.813	0.647	0.317	0.742	0.775	0.330	0.467	0.850	0.855
Shuttle	0.216	0.267	0.204	0.365	NA	0.291	0.000	0.326	NA	0.305	0.591	NA	NA	0.488	0.513
Mice Protein	0.431	0.392	0.424	0.624	0.324	0.530	0.542	0.592	0.437	0.543	0.532	0.411	0.405	0.668	0.656
Rank	7.9	9	10.2	9.4	12.6	6.6	6.5	6.7	10.4	7.6	5	10	10	2.7	1.9

For each dataset, the maximum achieved NMI is highlighted in bold. NA, not applicable.