Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data

Algorithm 1

MATLAB code 2: mining frequent itemsets.

(1)  s{1}=sum(bM); items{1}=find(s{1}≥suppn)^';s{1}=s{1}(items{1});

(2)  dum=bM^'*bM; [i1,i2]=find(triu(dum, 1)≥suppn); items{2}=[𝑖1𝑖2];

(3)  k=3

(4)  while ∼isempty(items{k−1})

(5)    items{k}=[]; s{k}=[]; ci=[];

(6)    for i=1:size(items{k−1},1)

(7)    vv=prod(bM(:,items{k−1}(i,:)), 2);

(8)   if k==3; s{2}(i)=sum(vv); end;

(9)   TID=find(vv>0);

(10)   pf=(unique(items{k−1}(find(ismember(items{k−1}(:,1:end −1),

(11)      items{k−1}(i,1:end −1), “rows”)), end)));

(12)  fi=pf(find(pf>items{k−1}(i, end)));

(13)  for jj=fi^'

(14)   j=find(items{1}==jj);

(15)   v=vv(TID).*bM(TID,items{1}(j)); sv=sum(v);

(16)     items{k}=[items{k}; [items{k−1}(i,:)items{1}(j)]]; s{k}=[s{k}; sv];

(17)  end

(18)  end

(19)   k=k+1

(20) end