7. Decision attribute contains two equivalence classes
U/Q = {{1,3,6}{2,4,5}}
With those elements belonging to the class possessing a
membership of one, otherwise zero
Normalize the given Dataset (conditional attribute)
8.
9. Using Normalized table, Calculate the values of
N and Z.
N = All Negative values change to Zero,
Z = 1- ( Absolute Value of Normalized Table),
Equivalence classes are
U/A = {Na , Za}
U/B = {Nb , Zb}
U/C = {Nc , Zc}
U/Q = {{1,3,6},{2,4,5}}
19. Similarly we find
From this it can be seen that attribute B will cause the greatest increase in
dependency degree.
20. Here,
P = {A,B}
U/A = {Na,Za}
U/B = {Nb,Zb}
U/P= U/A U/B = {Na,Za} {Nb,Zb}
U/P = {Na ∩ Nb, Na ∩ Zb, Za ∩ Nb, Za ∩ Zb}
21. Similarly find Decision Table for,
U/{B,C} ={Nb ∩ Nc, Nb ∩ Zc, Zb ∩ Nc, Zb ∩ Zc},
U/{A,B,C}= {(Na ∩ Nb ∩ Nc), (Na ∩ Nb ∩ Zc), (Na ∩ Zb ∩ Nc),
(Na ∩ Zb ∩ Zc ), (Za ∩ Nb ∩ Nc), (Za ∩ Nb ∩ Zc),
(Za ∩ Zb ∩ Nc), (Za ∩ Zb ∩ Zc)}
24. As this causes no increase in dependency, the
algorithm stops and outputs the reduct {A,B}.
The dataset can now be reduced to only those
attributes appearing in the reduct.