Algorithm 2.

Di-RAPOCC(D, K, α)

1: Input: Data matrix (D)

No. of co-clusters (KA and KB)

Minimum No. of rows in any co-cluster (α)

2: Output:: Two sets of discriminative co-clusters ({XA}, {XB})

3: Procedure:

4: Step 1: Compute δC for all the rows

5: $\forall x,y\in \left\{I\right\}{\delta }_C^A\leftarrow h_C^A(x,y)-h_C^B(x,y)$

6: $\forall x,y\in \left\{I\right\}{\delta }_C^B\leftarrow h_C^B(x,y)-h_C^A(x,y)$

7: Initialize each of the K co-clusters for each class

8: Compute SA and SB as defined in Equation (3)  / * Initialize rows and columns of each co-cluster * /

9: for k = 1 : K do

10:  ${\forall }_{m\in S_k^A}{X}_k^A.r\left(m\right)=1$, ${\forall }_{n\in N^A}{X}_k^A.c\left(n\right)=1$

11:  ${\forall }_{m\in S_k^B}{X}_k^B.r\left(m\right)=1$, ${\forall }_{n\in N^B}{X}_k^B.c\left(n\right)=1$

12: end for

13: Step 2: Update the row and the column clusterings

14: repeat

15:  for k = 1 : K do

16:   for i = 1 : M do

17:    $X_k^A.r\left(i\right)=\arg {\max }_{u\in \{-1,0,1\}}\Phi (X_k^A.r(i)=u)$

18:    $X_k^B.r\left(i\right)=\arg {\max }_{u\in \{-1,0,1\}}\Phi (X_k^B.r(i)=u)$

19:   end for

20:   for j = 1 : NA do

21:    $X_k^A.c\left(j\right)=\arg {\max }_{v\in \{0,1\}}\Phi (X_k^A.c(j)=v)$

22:   end for

23:   for j = 1 : NB do

24:    $X_k^B.c\left(j\right)=\arg {\max }_{v\in \{0,1\}}\Phi (X_k^B.c(j)=v)$

25:   end for

26:  end for

27: until convergence

28: Step 3: Merging similar co-clusters.

29: Step 4: Pruning.