Algorithm 1.

RAPOCC(D,κ, ℓ,K)

1: Input: Data matrix (D)

No. of row clusters (κ)

No. of column clusters (ℓ)

No. of optimized co-clusters (K)

2: Output:: A set of K co-clusters ({X})

3: Procedure:

4: Step 1 : initialization

5: i ← 1, j ← 1

6: ρ(g) ← i, ∀${\left[g\right]}_1^m$

7: γ(c) ← j, ∀${\left[c\right]}_1^n$

8: while i < κ or j < ℓ do

9:  if i < κ then

10:   i ← i + 1

11:   $\alpha \leftarrow \arg {\min }_{\alpha }{\sum }_{j=1}^{\ell }{H}^{\prime }(u,v):\rho \left(u\right)=\alpha ,\gamma \left(v\right)=l$

12:   Partition α using bisecting clustering algorithm

13:  end if

14:  if j < ℓ then

15:   j ← j + 1

16:   $\beta \leftarrow \arg {\min }_{\beta }{\sum }_{i=1}^{\kappa }{H}^{\prime }(u,v):\rho \left(u\right)=i,\gamma \left(v\right)=\beta$

17:   Partition β using bisecting clustering algorithm

18:  end if

19: end while

20: Step 2 : core co_clustering

21: repeat

22:  / * Row clustering * /

23:  for a = 1 : M do

24:   ρ(a) = arg maxu∈{−κ,…,−1,0,1,…,κ} HP(ρ(a) = u, γ)

25:  end for

26:  / * Column clustering * /

27:  for b = 1 : N do

28:   γ(b) = arg maxb∈{0,1,…,ℓ} HP(ρ, γ(b) = v)

29:  end for

30: until convergence

31: Step 3 : Merging similar co_clusters and refinement

32: Step 4 : Pruning