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

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

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N.Y.U.S.T.

I. M.Outline

Motivation Objective Methodology Experiments Conclusion Comments

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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.

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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.

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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

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N.Y.U.S.T.

I. M.Methodology

Data Preprocessing To convert nominal variables to numeric values.

The preprocessing function in SOM.

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N.Y.U.S.T.

I. M.Methodology SOM output map of 229 schools.

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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 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.

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N.Y.U.S.T.

I. M.Methodology The SOM output map of the 229 schools grouped into five clusters.

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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<0.0001.

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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.

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N.Y.U.S.T.

I. M.Experiments

Extended SOM

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N.Y.U.S.T.

I. M.Experiments

K-means

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N.Y.U.S.T.

I. M.Experiments

Factor/K-means

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N.Y.U.S.T.

I. M.Experiments

kNN

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N.Y.U.S.T.

I. M.Experiments

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Compare the total variance of the four clustering approaches.


N.Y.U.S.T.

I. M.Experiments

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


N.Y.U.S.T.

I. M.Experiments

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The peer schools of CSULB identified by the four methods.


N.Y.U.S.T.

I. M.Conclusion

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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.


N.Y.U.S.T.

I. M.Comments

2020

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 .

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

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