1. Comparisons of Clustering Detection and Neural Network in E-Miner, Clementine and I-Miner
Jong-Hee Lee and Yong-Seok Choi

2. CONTENTS Introduction Clustering Detection Neural Network Conclusion
References

3. Introduction The process of discovering unknown relationships and
Data Mining
The process of discovering
unknown relationships and
patterns in data
(Berry and Linoff, 1997)

4. Data mining tools are use widely to solve real problems.
Data mining software vendors increase.
Data mining tools are updated rapidly.
Rapidly-updated tools are needed objective
comparisons.

5. IBM/Intelligent Miner for data 6.1
SPSS/Clementine 5.2
SAS/Enterprise Miner 3.0
IBM/Intelligent Miner for data 6.1

6. Clustering Detection K-MEANS Kohonen Map x2 x1 Seed 1 Seed 2 Seed 3
입력계층
코호넨 출력계층
Kohonen Map

7. Order of Clustering Detection‘
Algorithms
Results
1st Options
2nd Options

8. Algorithms of Clustering Detection
1st Options
Results
2nd Options
Algorithms of Clustering Detection
ALGORITHMS
MINING TOOLS
Clementine
E-Miner
I-Miner
K-means O X
Kohonen Map
Demographic

9. Elimination of Outliers
Algorithms
1st Options
Results
2nd Options
Options of K-MEANS
OPTIONS
MINING TOOLS
Clementine
E-Miner
Standardization X O
Elimination of Outliers
Stopping Criterion
Missing Value
Number of Clusters
Categorical Data

10. # of Rows and # of Columns
Algorithms
1st Options
Results
2nd Options
Options of Kohonen Map
OPTIONS
MINING TOOLS
Clementine
E-Miner
I-Miner
Learning Rate O X
Stopping Criterion
# of Rows and # of Columns
Normalized Input
Neighborhood

11. Results of Clustering Detection
Algorithms
1st Options
Results
2nd Options
Results of Clustering Detection
RESULTS
MINING TOOLS
Clementine
E-Miner
I-Miner
Clusters O
Distances
Variables X

12. Neural Network MLP (Multi – Layer Perceptron ) RBF(Radial Basis
입력계층
은닉계층
출력계층
MLP (Multi – Layer
Perceptron )
입력계층
은닉계층
출력계층
RBF(Radial Basis
Function )

13. Order of Neural Network
Algorithms
Options
Results

14. Algorithms of Neural Network
Options
Results
Algorithms of Neural Network
ALGORITHMS
MINING TOOLS
Clementine
E-Miner
I-Miner
MLP(Multilayer Perceptron) O
RBF(Radial Basis Function)

15. Options of Neural Network
Algorithms
Options
Results
Options of Neural Network
OPTIONS
MINING TOOLS
Clementine
E-Miner
I-Miner
Learning Rate O
Momentum
Stopping Criterion
Normalize Input X
# of Hidden Layer
Set Random Seed

16. Results of Neural Network
Algorithms
Options
Results
Results of Neural Network
RESULTS
MINING TOOLS
Clementine
E-Miner
I-Miner
Sensitivity Analysis O X
Confusion Matrix
Statistics

17. Conclusion potential purchasers Criterion of Comparisons of
Data Mining tools
potential
purchasers
Data mining
company

18. References
[1] Berry, M. J. A. and Linoff, G.(1997). Data Mining Techniques for marketing, Sales, and Customer Support, New York: John Wiley & Sons, Inc.
[2] IBM Corp.(1999). IBM Intelligent Miner for Data Using the Intelligent Miner for Data Version 6 Release 1.
[3] Integral Solutions Ltd.(1998). Clementine User Guide Version 5.
[4] SAS Institute Inc.(1999). Enterprise Miner Reference