The application of SOM as a decision support tool to identify AACSB peer schools

1. The application of SOM as a decision support tool to identify AACSB peer schools Presenter : Chun-Ping Wu Authors :Melody Y. Kiang, Dorothy M. Fisher, Jeng-Chung Victor Chen , Steven A. Fisher , Robert T. Chi 國立雲林科技大學 National Yunlin University of Science and Technology DSS 2009

2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

3. Motivation • To assist schools in identify the “AACSB comparable peers”. • AACSBrequires a business school to identify a minimum of six comparable schools.

4. Objective • To combine and present the results from different clustering methods in an integrated manner. • To identify AACSB peer schools.

5. Methodology • Identify eleven attributes from the AACSB database. • 1) Degree Offered (Undergraduate/Masters/Doctoral) • 2) Private/Public and Commuter/Residential • 3) Carnegie Classification • 4) Endowment • 5) Ratio of Budget to Full Time Equivalent Faculty • 6) MBA Degree Confirmed • 7) Total Full Time Equivalent Faculty • 8) Ratio of Full Time Faculty Doctorate to Full Time Faculty • 9) Ratio of Full Time Equivalent Faculty to Full Time Faculty, • 10) MBA tuition • 11) GMAT score

6. Methodology • Data Preprocessing • To convert nominal variables to numeric values. • The preprocessing function in SOM.

7. Methodology • SOM output map of 229 schools.

8. Methodology • We applied the extended SOM method to further group the 229 schools into five. The detailed process is described in the following: • Step1 • Step2 • Assign a group number to each nodei, if |nodei|>0,and update the corresponding centroid value. • Step3 • Step4 • Step5 • Repeat step 4 until only one cluster or the pre-specified number of clusters has been reached.

9. Methodology • The SOM output map of the 229 schools grouped into five clusters.

10. Methodology • Aone-way analysis of variance(ANOVA) • Statistically significant differences are detected for all attributes among five clusters at p<0.0001.

11. Methodology • To compare the peer schools found by SOM with that of other popular clustering methods. • Extended SOM • K-means • Factor/K-means • kNN • We selected California State University, Long Beach(CSULB) as an example.

12. Experiments • Extended SOM

13. Experiments • K-means

14. Experiments • Factor/K-means

15. Experiments • kNN

16. Experiments Compare the total variance of the four clustering approaches.

17. Experiments The Euclidean distances of all the selected CSULB peer schools by the four methods.

18. Experiments The peer schools of CSULB identified by the four methods.

19. Conclusion • The SOM map can be used to integrate clustering results from any type of clustering methods. • The SOM is a valuable decision support tool that helps the decision maker visualizes the relationships among inputs. 19

20. Comments • Advantage • Providing a graphical interface to help candidate schools to visualize the relationship among the schools. • Drawback • The total within cluster variances are too high. • Application • enterprises ‘ Competitive analysis . 20