Table 3.

Parameter settings in other algorithms.

Settings	Other algorithms
	Classic fuzzy clustering	Multi-task clustering		Co-clustering	Semi-supervised clustering	Supervised clustering
	FCM	LSSMTC	CombKM	DRCC	CKM	SFCM
Core parameters	The fuzzifier $m=\frac{min(N,D-1)}{min(N,D-1)-2}$, where N and D are the data size and data dimension, respectively; C equals the number of clusters.	Task number T = 2; Regularizer l * ∈ {2, 22, 23, 24} ∪[100: 100: 1000]; Regularizer λ* ∈ {0.25, 0, 5, 0.75}	K equals the number of clusters	Regularizer λ* = μ* ∈ {0.1,1,10, 100,500,1000} See [51] for the detailed parameters	K equals the number of clusters	The fuzzifier $m=\frac{min(N,D-1)}{min(N,D-1)-2}$, where N and D are the data size and data dimension, respectively;

Note:

* denotes that the optimal settings need to be eventually determined by the grid search.