Intelligent Database Systems Lab N.Y.U.S.T. I. M. The application of SOM as a decision support tool...
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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.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
SOM output map of 229 schools.
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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 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.
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<0.0001.
10
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
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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
1919
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
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 .