Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 DSS 2009 國立雲林科技大學 National Yunlin University of Science and Technology 1

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiments Conclusion Comments 2

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation To assist schools in identify the “AACSB comparable peers”. AACSB requires a business school to identify a minimum of six comparable schools. 3

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To combine and present the results from different clustering methods in an integrated manner. To identify AACSB peer schools. 4

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 5

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Data Preprocessing  To convert nominal variables to numeric values. The preprocessing function in SOM. 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology SOM output map of 229 schools. 7

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 node i, if |node i |>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. 8

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology The SOM output map of the 229 schools grouped into five clusters. 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology A one-way analysis of variance(ANOVA)  Statistically significant differences are detected for all attributes among five clusters at p<

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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. 11

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Extended SOM 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments K-means 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Factor/K-means 14

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments kNN 15

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 16 Compare the total variance of the four clustering approaches.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 17 The Euclidean distances of all the selected CSULB peer schools by the four methods.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 18 The peer schools of CSULB identified by the four methods.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion 19 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.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments 20 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.