1. Data Recovery of Bioinformatics Microarray

2. Missing value prediction
Bio Data
IPO
Preprocess Phase
User-based Method
Normalization(未實作)
Data discritization
Prediction Phase
Rough Set Method
Missing value prediction

3. Example – Preprocess Phase (1/2)
Original data matrix
Data matrix after user-based method
M 1
M 2 M3 M4
M 5 U1
0.4 T ?
0.2 U2
0.3
0.1 U3
0.5 U4 U5 U6 U7 U8 U9
U10
M 1
M 2 M3 M4
M 5 U1
0.4 T
0.3
0.2 U2
0.1 U3
0.5 U4 U5 U6 U7 U8 U9
U10

4. Example – Preprocess Phase (2/2)
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
( )/3 = 0.1 U2
-0.4
( )/3 = -0.1 M1 M2 M3 M4 M5 U1 3 4 -1 -3 2 U2 -2 1

5. Example – Prediction Phase ( user size=2 ,item size=3)
Data matrix after data preprocess
M 1
M 2 M3 M4
M 5 U1 4 T 3 2 U2 1 U3 5 U4 U5 U6 U7 U8 U9
U10

6. Example – Prediction Phase ( user size=2 ,item size=2)
Class Selection: Compute similarity with target item by Pearson Correlation Coefficient
M 1
M 2 M3 M4
M 5 U1 4 T 3 2 U2 1 U3 5 U4 U5 U6 U7 U8 U9
U10

7. Equivalence class sets: Matrix/ {M2,M3} = {U4}1,2 {U8} 1,1 {U6} 3,1 5 U5
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
M 1
M 2 M3 M4
M 5 U3 5 1 U4 2 U6 3 U7 4 U8
User group:
{U1,U2,U5,U9,U10}
The lower approximation:
{U2,U5,U9, U1} 4,3
{U10, U1} 5,3
The set {U10, U1} 5,3 discard by constraint
The value will be 4

8. Data Set - diauxic

9. Data Set - ELU

10. Data Set - Phosphate