1. A Comparison of Capabilities of Data Mining Tools
Jong-Hee Lee, Yong-Seok Choi
Dept. of Statistics
Pusan National University

2. 1.Introduction The exploration and analysis
of large quantities of data
in order to discover meaningful
patterns and rules
(Berry and Linoff, 1997, p. 5)
Data Mining(DM)

3. Data Mining Tools for DM
There are many commercial DM tools (
DM tools vendors increase.
DM tools are updated rapidly.
Some comparisons :
Abbott et al.(1998) , Elder and Abbott(1998): out of date !
Some companies : not objective !
Rapidly-updated tools are needed objective
comparisons..

4. Tools Selected for our comparison
Product
Company
Version
Tested
Clementine
SPSS
5.2
Enterprise Miner
SAS Institute
3.0
Intelligent Miner
IBM
6.1

5. Comparisons of DM tools
2. Constitution of Main Windows and Nodes
3. Techniques
3.1 Market Basket Analysis
3.2 Decision Tree

6. 2. Constitution of Main Windows and Nodes
Comparative Criteria
Visualization using Streams
Main Windows
Nodes
In order of using
According to purpose
of modeling

7. Visualization using Streams
Main Window of Clementine
In order of using
Visualization using Streams

8. Visualization using Streams
Main Window of Enterprise Miner
In order of using
Visualization using Streams

9. Main Window of Intelligent Miner
According to purpose
of modeling
In order of using

10. Conclusion of comparison for Constitution of Main Windows and Nodes
(excellent)
Clementine
(poor)
Intelligent Miner
Main Windows
(excellent)
Intelligent Miner
Nodes

11. 3. Techniques 3.1 Market Basket Analysis 3.2 Decision Tree
For the other techniques , see J.H.Lee(2000).

12. 3.1 Market Basket Analysis
(MBA)
gives insight into the
merchandise by telling us
which products tend to be
purchased together
(Berry and Linoff,
1997, p.124)

13. Comparative Criteria for MBA
Market Basket Analysis
Algorithm
option
Result

14. Algorithm of MBA Mining Tool Clementine Algorithm Intelligent Miner
Enterprise ○
Association Rule ×
Sequential Pattern ○

15. Market Basket Analysis
Mining Tool
Clementine
Option
Intelligent
Miner
Enterprise ×
Minimum of Confidence ○
Minimum of Support ○
Minimum of Coverage × ○
Maximum of Items ×
Item Constraints ○

16. Market Basket Analysis
Mining Tool
Clementine
Result
Intelligent
Miner
Enterprise ○
Confidence ×
Support
Lift ○
Coverage × ×
Textual Display ○ ○
Visualization ×

17. Data Format in MBA ID 132 A 132 428 ID A B C D ... 132 B Y Y 428 C Y Y
< Horizontal Format >
132 A
132
428 ID A B C D
...
132 B Y Y
428 C Y Y Y
428
< Vertical Format > D
428 B
Upper tables cited at the xore web site( Exclusiveore.com/index.html)

18. Conclusion of comparison for MBA
(poor)
Clementine
Algorithm
(excellent)
Intelligent Miner
(poor)
Clementine
Option
(excellent)
Intelligent Miner
(poor)
Clementine
Result

19. 3.2 Decision Tree
Decision Tree
is for classification
and prediction

20. Comparative Criteria for Decision Tree
Algorithm
option
Result

21. Decision Tree Mining Tool Clementine Algorithm Intelligent Miner
Enterprise ×
CHAID ○
CART
C4.5 or C5.0 ○
ID3 × ×
SPRINT ○

22. Misclassification Costs
Decision Tree
Mining Tool
Clementine
Option
Intelligent
Miner
Enterprise ×
Misclassification Costs ○
Priors ○
Pruning Severity ○
Stopping Rule ○
Missing Value

23. Decision Tree Mining Tool Clementine Result Intelligent Miner
Enterprise ×
Tree View ○ ×
Confusion Matrix ○

24. Conclusion of comparison for Decision Tree
(excellent)
Enterprise Miner
Algorithm
(excellent)
Enterprise Miner
(poor)
Clementine
Option
(poor)
Clementine
Result

25. Constitution of Main Windows
4.Concluding Remarks
Constitution of Main Windows
(excellent)
Clementine
(poor)
Intelligent Miner
Constitution of Nodes
(excellent)
Intelligent Miner

26. Market Basket Analysis
(excellent)
Intelligent Miner
(poor)
Clementine
Decision Tree
(excellent)
Enterprise Miner
(poor)
Clementine

27. Enterprise and University
A Comparison of
Capabilities of
DM tools
in this study
potential
Purchasers:
Enterprise and University DM
companies