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Bio Data
Preprocess Phase
User-based Method
Normalization(未實作)
Data discritization
Prediction Phase
Rough Set Method
Missing value prediction
Original data matrix
Data matrix after user-based method
M1
M2
M3
M4
M5
M1
M2
M3
M4
M5
U1
0.4
T
?
0.2
0.2
U1
0.4
T
0.3
0.2
0.2
U2
0.4
0.4
0.3
0.1
0.1
U2
0.4
0.4
0.3
0.1
0.1
U3
0.5
0.5
0.1
0.1
0.1
U3
0.5
0.5
0.1
0.1
0.1
U4
0.2
0.1
0.2
0.2
0.2
U4
0.2
0.1
0.2
0.2
0.2
U5
?
0.4
0.3
0.2
0.2
U5
0.4
0.4
0.3
0.2
0.2
U6
0.3
0.3
0.1
0.1
0.1
U6
0.3
0.3
0.1
0.1
0.1
U7
0.3
?
0.2
0.2
0.2
U7
0.3
0.4
0.2
0.2
0.2
U8
0.1
?
0.1
0.1
0.1
U8
0.1
0.1
0.1
0.1
0.1
U9
0.4
0.4
0.3
0.1
0.1
U9
0.4
0.4
0.3
0.1
0.1
U10
0.4
0.5
0.3
?
0.1
U10
0.4
0.5
0.3
0.1
0.1

Goal: Data transform
Function:

Example:

M1
M2
M3
M4
M5
Avg
Step
U1
0.3
0.4
-0.1
-0.3
0.2
0.1
(0.4-0.1)/3 = 0.1
U2
0.2
-0.1
0.2
-0.4
-0.3
-0.1 (-0.4+0.1)/3 = -0.1
M1
M2
M3
M4
M5
U1
3
4
-1
-3
2
U2
-2
1
-2
4
3
Data matrix after
data preprocess
M1
M2
M3
M4
M5
U1
4
T
3
2
2
U2
4
4
3
1
1
U3
5
5
1
1
1
U4
2
1
2
2
2
U5
4
4
3
2
2
U6
3
3
1
1
1
U7
3
4
2
2
2
U8
1
1
1
1
1
U9
4
4
3
1
1
U10
4
5
3
1
1

Class Selection:
Compute similarity with target item by
Pearson Correlation Coefficient
M1
M2
M3
M4
M5
U1
4
T
3
2
2
U2
4
4
3
1
1
U3
5
5
1
1
1
U4
2
1
2
2
2
U5
4
4
3
2
2
U6
3
3
1
1
1
U7
3
4
2
2
2
U8
1
1
1
1
1
U9
4
4
3
1
1
U10
4
5
3
1
1
M1
M2
M3
M4
M5
U1
4
T
3
2
2
U2
4
4
3
1
1
U9
4
4
3
1
1
U10
4
5
3
1
1
U5
4
4
3
2
2
M1
M2
M3
M4
M5
U3
5
5
1
1
1
U4
2
1
2
2
2
U6
3
3
1
1
1
U7
3
4
2
2
2
U8
1
1
1
1
1
Equivalence class sets:
Matrix/ {M2,M3} =
{U4}1,2
{U8} 1,1
{U6} 3,1
{U7} 4,2
{U2,U5,U9, U1} 4,3
{U3} 5,1
{U10, U1} 5,3
User group:
{U1,U2,U5,U9,U10}
The set {U10, U1} 5,3 discard
by constraint
The lower approximation:
{U2,U5,U9, U1} 4,3
{U10, U1} 5,3
The value will be 4
0.9
0.85
0.8
NRMSE
0.75
KNN
GOKNN
0.7
GAKNN
PAKNN
0.65
PGAKNN
0.6
RS-Based
0.55
0.5
1%
5%
10%
Missing Rate
15%
20%
Algorithm Comparison
1.2
1.1
KNN
NRMSE
1
GOKNN
GAKNN
0.9
PAKNN
0.8
PGAKNN
USER-BASED
0.7
C-USER-BASED
RS-BASED
0.6
Fixed_RS-based
0.5
1%
5%
10%
MIssing Rate
15%
20%
1.4
1.2
1
KNN
NRMSE
0.8
GO_KNN
GAKNN
0.6
PAKNN
PGAKNN
0.4
RS-based
0.2
0
1%
5%
10%
Missing Rate
15%
20%