A Type-Based Blocking Technique for Efficient Entity Resolution over Large-Scale Data

Algorithm 2. Attribute clustering algorithm.

Input: Map < attribute name, List >//this is for same type block

     δ: the threshold for the attributes similarity

Output: Set of attribute names clusters: C

(a)   connects ←{};

(b)   noOfAttributes ←the size of Map < attribute name, List >;

(c)   For (int i = 0; i < noOfAttributes; i++) do

(d)     For (int j = i + 1; j < noOfAttributes; j++) do

(e)      ←attribute[i].getSimilarAttribute(attribute[j]);//refer to the formula (1)~(4)

(f)      If ( > δ) then connects ← connects.add(new Connect(i, j));

(g)     End For(j)

(h)  End For (i)

(i)   cons ←computerTransitiveClosure(connects);

(j)   C←getConnectedComponents(cons);

(k)   For each do

(l)      If (.size() == 1) then C.remove();

(m)   End For ();

(n)     Return C;

Algorithm 2. Attribute clustering algorithm.