Classification and Retrieval of Images from Databases Using Rough Set Theory - Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications - page 188

Algorithm-1: Reduct (Minimal number of attributes)
Input: A decision table DT ( C, D ), where C : the set of all conditional attributes;
and D : the set of decisional attributes;
Processing:
Red←{}
Do
DT ←Red
Loop (

x C ∈ − Red)

if

{ } ( )

D > ( ) D

R x ∪ T

DT = ∪
Red ← DT
Until ( ( )

Red {x}

D = ( )) D

R C

Return Red
Output: Red: A set of minimum attribute subset ; Red ⊆ C

A dataset has at least one reduct in its reduct set—the trivial reduct (i.e., the dataset itself). It also
has one or more minimal reducts.

r ule generation and Building the Classifier

The main task of the rule generation method is to compute reducts relative to a particular kind of infor-
mation system. The process by which the maximum number of condition attribute values is removed
without losing essential information is called value reduction, and the resulting rule is called maxi-
mally general or minimal length. Computing maximally general rules is of particular importance in
knowledge discovery since they represent general patterns existing in the data. In this subsection, we
discuss a method to simplify the generated decision rules by dropping some condition attributes. The
rule generation algorithm is described as follows: (Refer to Algorithm-2).

Algorithm-2: Rule Generation Algorithm
Input: A set of specific decision rules RULE
Processing:
GRULE ←Φ
N ←|RULE|
For i=0 to N-1 do
r ← r i
M ← | r|
For j = 0 to M-1 do
Remove the j th condition attribute a j in rule r
If r inconsistent with any rule r ∈ RULE then restore the dropped condition a j
End if
End for
Remove any rule r ∈ GRULE that is logically included in rule r
If rule r is not logically included in a rule r ∈ GRULE then
GRULE ← r  GRULE