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Li € | C3 | C3 | d 
li X 0 0 0 
l5 0 X 0 0 
ls 0 0 X 0 
2, 0 X 1 0 
2, 0 1 X 0 
3, X 0 1 0 
RE 1 0 X 0 
4 ] l X | 
5i X 2 2 0 
5, ] X 2 0 
53 1 2 X 0 
6, X I 0 0 
65 2 X 0 0 
7 2 X I 1 
8 3 1 X 1 
9, X 2 1 0 
9, 3 X | 0 
93 3 2 X 0 
10; X 0 0 0 
10, 3 X 0 0 
10; 3 0 X 0 
  
  
Table 4. Expanded spatial information table 
It can be seen from Table 4 that decision rules 4, 7 and 8 have 
only one reduction form, that decision rules 2, 3 and 6 have two 
reduction forms, and that decision rules 1, 5, 9 and 10 have 
three reduction forms. Thus,the knowledge expression system 
has (1x1x1)x(2x2x2)x(3x3x3x3)=648 reduction forms. 
According to the practical and effective principles, the largest 
weights of the rules are 1, 25, 3, 4, S5 6; 7, 8, 05, 10; 
respectively. Then, we can obtain the reduced practical decision 
rule as follows. 
c,(0)c (00 Ve ,(0)c»( 1)Vc;(1) e»0)Vc,C1) c(2)Ve,(2) cs(0) Vc,(3) 
ca(2)Ve,(3) c2(0) +0 
e(1) ed) Ve,(3) cll)Ve(2) st) 
6. CONCLUSIONS 
Rough Set Theory has been widely used in KD(knowledge 
discovery) since it was put forward. Having important 
functions in the expression, study, conclusion and etc. of the 
uncertain knowledge, it is a powerful tool which sets up the 
intelligent decision system. Actually, many knowledge systems 
are so rough that they make an obvious delay in the response 
time of an Intellectual Processing System by being put into a 
knowledge database directly. So, it is necessary to refine the 
knowledge which is extracted further. This article discussed 
how to express knowledge within an Information System with 
conditions-decisions forms in DSS, to get the potentialy 
reduced rules of decision-making by using Rough Set, spatial 
information tables on the basis of this and combining with 
analysis and reasoning with the priori knowledge from 
decision-makers, then to obtain a group of reasonable decision- 
rule sets by using the practical and effective principles, and 
finally to solve the problems of obtaining the decision-rules in 
DSS. 
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