A Comparison of Capabilities of Data Mining Tools

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

1. Introduction Data Mining(DM) 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 Tools for DM There are many commercial DM tools (http: //kdnuggets. com). 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 (1998) , Elder and Abbott(1998): out of date !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. .

Tools Selected for our comparison Product Company Version Tested Clementine SPSS 5. 2 Enterprise Miner SAS Institute 3. 0 Intelligent Miner IBM 6. 1

Comparisons of DM tools 2. Constitution of Main Windows and Nodes 3. Techniques 3. 1 Market Basket Analysis 3. 2 Decision Tree

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

Main Window of Clementine Visualization using Streams In order of using

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

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

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

3. Techniques 3. 1 Market Basket Analysis 3. 2 Decision Tree For the other techniques , see J. H. Lee(2000).

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)

Comparative Criteria for MBA Market Basket Analysis Algorithm option Result

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

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

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

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

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

3. 2 Decision Tree is for classification and prediction

Comparative Criteria for Decision Tree Algorithm option Result

Decision Tree Algorithm Clementine Mining Tool Enterprise Miner Intelligent Miner CHAID × ○ × CART × ○ × C 4. 5 or C 5. 0 ○ ○ × ID 3 ○ × × SPRINT × × ○

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

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

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

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

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

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