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Nothing To Display - Clustering and Ranking University Majors using Data Mining and AHP algorithms: A case study in Iran
 
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Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
113
Clustering and Ranking University Majors using Data Mining
and AHP algorithms: A case study in Iran
Abbas Rad*
Abolfazl Kazzazi**
Mohammad Soltani***
Davoud Talebi****
Abstract
Although all university majors are prominent and the necessity of their
presences is of no question, they might not have the same priority basis
considering different resources and strategies that could be spotted for a
country. Their priorities likely change as time goes by; that is, different
majors are desirable at different times. If the government is informed of
which majors could tackle today existing problems of the world and the
country, it surely would esteem those majors more. This paper considers
the problem of clustering and ranking university majors in Iran. To do so,
a model is presented to clarify the procedure. Eight different criteria are
determined and 177 existing university majors are compared on these
criteria. First, by K-means algorithm, university majors are clustered
based on similarities and differences. Then, by AHP algorithm, we rank
university majors.
Keywords: data mining; clustering; K-means algorithm; multi-criteria
decision making; analytic hierarchy process; university major ranking
problem
* PhD candidate of industrial engineering of Amir Kabir University of Technology
** Faculty member of Allameh Tabatabaee University
*** EMBA graduate of Azad University of Bonab
**** PhD graduate in industrial engineering of University of Shahid Beheshti
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
114
Introduction
University major choice is an important decision to make for
anybody seeking professional/higher education. It is a decision that
will influence the way people look at the world around themselves
(Porter & Umbach, 2006). The future occupation of people is
closely related to their education. Given this importance, it is
always of interest to find the guidance in collaboration with making
aforementioned choices about which major to select. It is known
that students should draw on available resources to ultimately pick
a path that is right for them (Boudarbat, 2008). Nowadays, due to
the creation of numerous undergraduate majors, the need for having
a more precise approach becomes increasingly necessary. Besides
individual reasons, governments could be another client of
university major choice. They might look for a way to supply their
professional labors as one of the most influential factors in their
national future. To manage this and to find which majors are of
more importance in future, they require a systematic approach to
have a deeper view about majors. For example, they entail to know
areas each major affects, how majors can affect, to what extent
each major is influential in a given area. Although all university
majors are prominent, and the necessity of their presences is of no
question, they might not have the same priority basis considering
different strategies that could be spotted for a country. Their
priorities likely change as time goes by; that is, different majors are
desirable at different times. If the government is informed of which
majors could tackle today existing problems of the world and the
country, it surely would esteems those majors more. By investing
more on those majors or providing greater grants for those studying
the majors, they intend to motivate more talented students to study
them.
Therefore, with reference to the given explanations, it is a handy
contribution to construct a model for such a decision-making
process. To this end, we define eight different Main Specialization
Groups (MSG). We first group university majors based on their
similarities and differences which are obtained by their magnitude
of influence on MSGs. The values of different major group can
Clustering and Ranking University Majors using Data Mining and AHP algorithms
115
then be calculated and evaluated to provide useful decisional
information for the government to utilize resources rationally.
Among available grouping methods, data mining approaches have
attracted more attention. Given different data mining models,
clustering is regarded as the art of systematically finding groups in
a data set (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). In this
paper, to cluster the university majors, we utilize the k-means
algorithm as the most widely used method that has shown much
success in different applications such as market segmentation,
pattern recognition, information retrieval, and so forth (Cheung,
2003; Kuo, Ho, & Hu, 2002). Besides its high performance, it is a
very popular approach for clustering because of its simplicity of
implementation and fast execution.
Ranking/ordering university majors is a multi-criteria problem;
that is, different criteria should be taken into account. For example,
one major might be very important for industrial setting while
another one is appropriate for improving social culture. Armed with
this, we apply the Analytic Hierarchy Process (AHP) as a simple
Multi Criteria Decision Making (MCDM) method for dealing with
unstructured, multi-attribute problems. AHP is developed by Saaty
and widely studied by other authors (Bolloju, 2001; Kablan, 2004;
Lipovetsky & Conklin, 2002). It consists of breaking down a
complex problem into components, which are then organized into
levels in order to generate a hierarchical structure. The aim of
constructing this hierarchy is to determine the impact of the lower
level on an upper level, and this is achieved by paired comparisons
provided by the decision-maker. The hierarchical structure of the
AHP model attempts to estimate the impact of each alternative on
the overall objective of the hierarchy. Another advantage of the
AHP is that it uses a consistency test to filter inconsistent
judgments. Taking into account these advantages, many
outstanding works have been published based on AHP. They
include applications of AHP in different fields, such as planning,
selecting the best alternative, ranking alternatives as in our case,
resource allocation, resolving conflicts, optimization, etc., as well
as numerical extensions of AHP (Chatzimouratidis & Pilavachi,
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
116
2009; García-Cascales & Lamata, 2009). An important
bibliographic review of MCDM tools was carried out by Steuer
(2003). Our objective is to employ an AHP application in the
problem of ranking university majors.
Looking into the literature, there is no paper published dealing
with the major choice as a nationwide problem. They almost tackle
the problem as just an individual assistance model. These papers
usually propose regression models that guide a student to know
which major is the best choice regarding her/his personal
conditions, characteristics and interests (Berger, 1988; Boudarbat,
2008; Crampton, Walstrom, & Schambach, 2006; Porter &
Umbach, 2006). As far as we reviewed, this paper is the first work
exploring this problem as a nationwide one, and clustering
university majors using a data mining method called k-means.
Moreover, university majors are ranked by a MCDM method,
called AHP algorithm.
In the following parts of this paper, first university majors are
clustered, and then the conceptual model of university major
ranking is provided. Finally, AHP algorithm is applied to order
university majors.
University major ranking model
This section presents a conceptual model to describe the
decision making procedure of university major clustering and
ranking. In fact, we employ a Flow Chart (FC) model to show the
whole procedure. This diagram is to clarify each step of the whole
procedure regardless of its details. Figure 1 presents the FC model.
The procedure could be divided into three main phases: data
gathering, data preparation, and decision making.
In the first phase, the list of existing university majors is
solicited from Iranian Ministry of Science, Research, and
Technology. University majors in Iran are presented in five main
groups each of which covers an educational background from high
school. These five groups are: fine arts, mathematics and physics,
empirical sciences, human sciences, and foreign languages. Finally,
177 university majors presented in Iran are identified. Then, MSGs
Clustering and Ranking University Majors using Data Mining and AHP algorithms
117
are determined. Doing so, this paper intends to consider eight
highlighted main specialization groups with due considerations to
Iran’s own attributes and special areas are needed in order to ease
the design process of sustainable development. These eight MSGs
were extracted after a review of the literature of the problem and
the reports published by the local government for achieving
sustainable development, and the validity and reliability of these
MSGs have been verified and confirmed by a number of structured
interviews. At this time, additional rules and constraints taken from
Iran’s strategies and views are to be considered as well. Finally, the
following eight MSGs are considered as decision criteria:
• financial/economical • social/religious
• industrial • political
• service • agricultural
• therapy/health • environmental/natural
resources
In the second phase, regarding the data gathered in previous
phase, two suitable questionnaires are designed. The first one is to
compare university majors on their magnitude of influence on
abovementioned MSGs. The second one is to compare the
importance/weight of each MSG for Iran's current situation. The
questionnaires are sent to 64 experts whose definition is set in this
research as follows: An expert is a person who has at least an MS
degree in one of the official university majors along with at least
three-year working experience in his/her specialization field. After
data collection, some qualification tests, such as consistency in
AHP algorithm, are utilized to verify the results of questionnaires.
If all the requirements are met, the third phase starts. First, we
employ one of the well-known data mining approaches to cluster
university majors based on their similarities and differences on the
results. This step is explained in more details in the next section.
Then, we rank university majors by means of a MCDM algorithm.
There are two options to employ: Multi-Objective Decision Making
(MODM), or Multi-Attribute Decision-Making (MADM)
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
118
approaches. MODM models are those searching a
continuous/integer space to find optimal solutions. The most
commonly used type of these models is linear programming.
Figure 1 - General model of clustering and ranking university
majors
Since the problem of ranking university majors is not a
continuous problem, the MODM model is not the best choice. Our
purpose to present the model is to mathematically characterize the
problem. A MADM model could be more effective. Among the
MADM approaches, AHP has shown many successful applications
in such ranking problems (Chen & Cheng, 2009; Hsu & Pan,
2009). Therefore, we have been thinking of ranking the university
Data preparation
Cluster UMs
using K-means
Do data meet
all the
requirements?
Rank UMs (clusters)
using AHP
Determine Main
Specialization Groups
(MSG) as criteria
Identify rules and
constraints
List existing
university majors
(MU) as alternatives
Design a suitable
questionnaire to weight
MUs on MSGs
Design a suitable
questionnaire to weight
MSGs
Yes No
Clustering and Ranking University Majors using Data Mining and AHP algorithms
119
majors by AHP algorithm. The details and results are presented
further in the paper.
University Major Clustering Problem (UMCP) in Iran
The background of clustering and K-means algorithm
In today's world, data are considered as one of the most valuable
assets. With the current dramatic increase in magnitude of available
data and also their low cost storage, it became interesting to
discover knowledge in these data. Therefore, the importance of
how to effectively process and use data more and more soars. This
calls for new techniques to help analyze, understand the huge
amounts of stored data (Liao & Chen, 2004). Among the new
techniques developed, data mining is the non-trivial extraction of
hidden and potentially useful information from large sets of data. In
other words, data mining is the process of discovering significant
knowledge, such as patterns, associations, changes, anomalies and
significant structures from large amounts of data stored in
databases, data warehouses, or other information repositories (Liao,
Chen, & Wu, 2008). In the literature, there are many data mining
models such as classification, estimation, predictive modeling,
clustering, affinity grouping or association rules, description and
visualization, as well as sequential modeling.
Clustering is a widely used technique, whose goal is to provide
insight into the data by partitioning the data (objects) into disjoint
and homogeneous groups (clusters) of objects, such that objects in
a cluster are more similar to each other than to objects in other
clusters. According to Boutsinas and Gnardellis (2002), clustering
algorithms have been frequently studied in various fields including
machine learning, neural networks and statistics, among others
(Corcho, Lopez, & Perez, 2003; Davies & Fensel, 2003; Fensel,
2001).
The k-means algorithm, first proposed by MacQueen (1967), is
the most popular partition-clustering method that has attracted great
interest in the literature. The goal of the k-means algorithm is to
partition the objects into k clusters so that the within-group
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
120
similarity is maximized. The procedure of k-means methods could
be described as follows.
• Place k points into the space represented by the objects that
are being clustered. These points represent initial group centroids.
• Assign each object to the group that has the closest centroid.
• When all objects have been assigned, recalculate the
positions of the k centroids.
• Repeat Steps 2 and 3 until the centroids no longer move. This
produces a separation of the objects into groups from which the
metric to be minimized can be calculated.
The applications of K-means for university major
clustering problem
This study employs k-means in cluster analysis and partitions
177 university majors into ten clusters. The distance between each
major and centeriod is calculated using Euclidean distance as the
most commonly used distance measure in k-means method (Huang,
Chang, & Wu, 2009). Again, each major is assigned to the nearest
cluster, and the new centeriod dimension j of cluster l is the
arithmetic mean of the influence degree of the majors belonging to
the cluster l. This procedure iterates until no new cluster is obtained
when majors are reassigned. To run the procedure, the algorithm
was coded in MATLAB 7.
The results of UMCP and the final centriods of the ten clusters
are presented in Tables 1 and 2, respectively. Centeriods of each
cluster are its average magnitude of influence on each MSG.
Cluster 1 includes the majors concerning more on financial and
economical MSG, and slightly on social and religious MSG.
Cluster 2 consists of engineering majors; therefore, they clearly
focus on the industrial MSG. Majors in Cluster 3 are those relating
to individual therapy and health, whereas majors of Cluster 4 are
those focusing on public health. Cluster 5 covers majors training
social related courses such as social, political, religious and
military affairs. Cluster 6 involves majors providing services for
civilians. Cluster 7 includes majors relating to agriculture and
natural resources MSG. Cluster 8 consists of majors that their
Clustering and Ranking University Majors using Data Mining and AHP algorithms
121
aspects of social services are more influential than the other
aspects. Cluster 9 involves majors that could almost affect all the
eight criteria; although they are more important on economical,
social and religious, political, and service criteria. Cluster 10
apparently covers majors that have been given lower values by the
involved decision makers. Based on results collected, they might be
comparatively less influential.
Table 2 - The centriods of university major clusters in each MSG in
percentage
cluster
MSG
financial/
economical
social/
religious
industry
politics
services
therapy/
health
agriculture
environment/
natural resources
1 44 28 18 4 11 2 5 10
2 59 9 70 6 41 18 21 16
3 11 14 0 0 47 4 58 15
4 16 54 4 5 25 8 37 27
5 7 37 4 41 8 1 2 1
6 18 5 18 1 20 8 10 10
7 29 2 23 4 16 44 35 54
8 6 20 4 10 8 2 5 3
9 41 59 19 50 24 6 5 6
10 4 2 3 2 6 1 3 3
University Major Ranking Problem (UMRP) in Iran using
AHP
The first step in using the AHP is to construct a hierarchy
structure of the UMRP. Figure 2 depicts how the problem
mentioned above can be modeled using the AHP. This hierarchical
structure offers a natural way to divide and conquer the complexity
of UMRP; without it, decision makers may simply be
overwhelmed. The hierarchy structure of the UMRP is three-level
one. The first is final problem’s goal “ranking university majors”.
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
122
The second includes the involved criteria which are those eight
MSGs mentioned earlier, and the third consists of the problem’s
alternatives which are the 177 official university majors in Iran.
Figure 2 shows the hierarchy structure of the UMRP. It is necessary
to indicate that the consistency of each comparison matrix of each
decision maker is tested in data preparation phase. In the case of
inconsistency proceeding, the decision maker is requested to revise
the judgments.
Since UMRP is a group decision making process and many
judgments are involved, we need to utilize an indicator combining
the judgments. To this end, we use an approach with following two
steps:
• The weights for each decision maker are obtained. Due to
high complexity size and excessive number of required
computations, utilization of standard form (i.e. Eq. (5)) to calculate
the weights seems ineffective. To effectively obtain the weights,
we employ an approximation method as follows. First, the
summation of each row of the comparison matrix is calculated.
Therefore, we have an n×1 matrix. Then, the normalized form of
this matrix gives the weights.
• To combine the weights obtained for each decision maker,
the arithmetic mean is used. These means represent the final weight
of each alternative in a given criterion. The final results for weights
of the eight criteria are as follows: finance and economics (0.346),
society and religion (0.196), industry (0.131), politics (0.041),
services (0.070), agriculture (0.070), medicine (0.072) and
environments and natural sources (0.074).
All the other steps of AHP are implemented and the final results
of the UMRP (majors’ ranks) are presented in Table 3.
Management, mechanical and information technology engineering
are three top majors, based on view of the decision makers. After
these three majors, engineering majors, such as industrial and civil
engineering along with medicine, film and video production and
law are more influential majors.
Sensitivity analysis allows us to verify the results of the
decision. A sensitivity analysis can be performed to see how
Clustering and Ranking University Majors using Data Mining and AHP algorithms
123
sensitively the alternatives change with the importance of the
criteria. To implement AHP analysis, we use specialized software,
called Expert Choice version 2000. The implementation of AHP
provides four graphical sensitivity analysis modes: dynamic,
gradient, performance and two-dimensional analysis. Performance
sensitivity analysis has been employed here. It depicts how well
each alternative performs on each criterion by increasing or
decreasing the importance of the criteria.
Level 1
Goal
Level 2
Criteria
Level 3
Alternatives
Figure 2 - Hierarchy structure of the UMRP
In view of the above, we consider that a sensitivity analysis is
necessary in order to analyze decision-making if the condition is
changed over time. Sensitivity analysis identifies the impact of
changes in the priority of criteria. It is clear that there are variations
in the relative importance/weights of the criteria. Due to numerous
number of university majors, we just continue the sensitivity
analysis with 11 first majors whose importance ratios are
significantly different from the others: management, mechanical
eng., information technology eng., industrial eng., medicine, film
and video production, petroleum eng., law, electrical eng., civil
eng., and bio eng. majors.
Ranking University Majors
MS
G 8
MS
G 7
MS
G 6
MS
G 5
MS
G 4
MS
G 3
MS
G 2
MS
G 1
UM
177
UM 3 UM 2 UM 1
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
124
With regard to financial/economical MSG, mechanical eng. goes
to the top if the FEG importance decreases; while management
keeps the top rank if financial/economical MSG’s importance
increases. If the relative importance becomes greater than 0.5,
information technology eng. obtains the second rank. Considering
social/religious MSG, film and video production is highly sensitive
towards social/religious MSG’s importance weight. More precisely,
its priority soars if social/religious MSG’s importance weight
increases to 0.55 while in lower weights, its rank deteriorates. The
same trend could be also seen from law. Engineering-based majors
show the opposite trend, meaning that they go down versus greater
weight of social/religious MSG’s importance. Taking into account
industrial MSG, as expected, engineering-based majors’ ranks soar
while higher weight is given industrial MSG importance. This time,
medicine, law, and film and video production lose their ranks and
fall down to lower priorities.
Regarding political MSG analysis, we can see film and video
production taking over all the other majors if decision makers when
political MSG importance is adjusted upwards the current weight.
Law is also granted the second rank if PG importance becomes
greater than 0.6. Considering Service MSG sensitivity analysis, it
seems medicine, information technology eng., and law are those
majors gaining higher priorities while increasing service MSG
importance. In therapy/health MSG analysis, medicine and bio.
eng. move to upper ranks if the decision makers give greater
relative importance to therapy/health MSG than that of the other
criteria. Actually, they have direct correlation with the importance.
The results of sensitivity analysis of agricultural criterion shows
that mechanical eng. is granted the first priority among these 11
majors when we consider a possible greater influence of this MSG.
Management degrades to fourth rank in the relative importance
greater than 0.57. In environmental/natural resources MSG,
petroleum eng. goes up to the first rank if the importance of this
MSG is decided to be greater than 0.22.
Clustering and Ranking University Majors using Data Mining and AHP algorithms
125
Conclusion and Suggestions for Future Studies
This paper dealt with university majors ranking problem. UMRP
is an important problem since university majors might not have the
same priority basis with due considerations to different resources
and strategies that a country has, though they are all eminent.
UMRP is a dynamic problem; therefore, a general model is needed
to clarify the whole procedure. In this case, we employed a Flow
Chart model which has three phases: data gathering, data
preparation, and decision making. In the first two phases, all the
data needed for the third phase are collected and tested for the
necessary requirements. In the third phase, the all the majors are
clustered according to their similarity and differences by k-means
algorithm. Since UMRP is an MADM problem, we employ an
application of AHP algorithm to rank university majors.
As a direction for future research, one might work on
application of other multi objective decision making procedure for
the problem under consideration. It is also interesting to present
other criteria that can influence the university major ranking.
Another impressive stream for future research is to introduce a
model for how to assign grants for the research projects defined for
each majors according to their ranks.
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Appendixes
Table 1 – The results of university major ranking problem
Clusters Majors
1 1 Historical
construction
renovation
2 historical
facility
renovation
3 architecture
4 handcraft
industry
5 fashion and
textile design
6 textile eng.
7 architecture
eng.
8 urbanization 9 economical
sci.
10 industrial
economics
11 theoretical
economics
2 1 electrical eng. 2 industrial eng. 3 civil eng.
4 computer eng. 5 information
tech. eng.
6 mechanical
eng.
7 material eng. 8 biomedical
eng.
9 electronic
eng.
3 1 medicine 2 dentistry 3 physiotherapy
4 midwifery 5 nursing 6 radiology
7 radiation
therapy
8 dental nursing 9 curator in
medicine
10 medical
services man.
11 work therapy
4 1 nutrition 2 environmental
health
3 professional
health
4 public health 5 psychology 6 clinical
psychology
5 1 journalism 2 judiciary
sciences
3 Islamic
jurisprudence
exp.
4 law affairs
exp.
5 Islamic
wisdom
6 social sci.
7 Islamic sci. 8 theology 9 Quran sci.
10 art and
cultural man.
11 political
relations
12 political sci.
13 national
security
14 security sci. 15 military
information
Clustering and Ranking University Majors using Data Mining and AHP algorithms
129
16 political
geography
17 military sci. 18 training
political
teachers
6 1 photography 2 industrial
design
3 railroad eng.
4 clerical affairs 5 banking
affairs man.
6 rural civil
eng.
7 food industry 8 insurance
man.
9 accounting
10 physical sci. 11 chemical sci. 12 mathematical
sci.
13 clinical
laboratory sci.
14 aerospace
eng.
15 polymer eng.
16 statistical sci. 17 nuclear tech. 18 robotics eng.
7 1 agricultural
eng.
2 veterinarian 3 agricultural
machine tech.
4 mining eng. 5 petroleum
eng.
6 oil eng.
7 oil
Exploration
eng.
8 biotechnology 9 biological
Sci.
10 chemical eng. 11 emergency
medical
affairs
12 herbaceous
production
tech.
13 geology 14 pharmacology 15 animal
production
tech.
16 forestry tech. 17 fishing tech. 18 tore tech.
19 environmental
tech.
20 urban forestry
eng.
21 general
biological
Sci.
22 medicinal
plant
production
23 entomology 24 natural
resources
eng.
8 1 traditional
arts
2 theatre 3 portrait
4 Iranian music 5 universal
music
6 social
activism
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
130
7 social
relations sci.
8 guidance &
consultation
9 tourist
services
10 Persian lan. &
lit.
11 Arabic lan. &
lit.
12 Arabic
translation
13 audiology 14 taxation
affairs
15 ECO
insurance
man.
16 national
operations
17 geography 18 history
19 archaeology 20 cultural sci. 21 speaking
therapy
22 family studies 23 postal sci. 24 weaponry
25 physical
training &
sports sci.
26 organizational
psychology
27 childhood
studies
28 philosophy
9 1 cinema 2 film & video
production
3 law
4 management
10 1 graphic
design
2 museums
guidance
3 museums
keeping
4 sculpture 5 carpet
expertise
6 visual arts
7 printmaking 8 marine eng. 9 aircraft
maintenance
eng.
10 aviation 11 shipping
navigation
12 technical
excavation
13 pilot 14 computer sci. 15 aircraft
navigation
16 helicopter
pilot
17 transportation
eco.
18 non-coal
mineral
extraction
19 wood industry 20 water sci. eng. 21 seeing ponder
22 prosthetics 23 medical
documents
24 anesthesia
25 surgery room 26 teeth 27 teeth health
Clustering and Ranking University Majors using Data Mining and AHP algorithms
131
prosthetics
28 marine
commissar
man.
29 marine
business man.
30 marine
security
31 curator 32 custom affairs 33 Turkish lan.
& lit.
34 English lan.
& lit.
35 English
translation
36 English
training
37 English news
translation
38 German lan.
& lit.
39 German
translation
40 French lan. &
lit.
41 French
translation
42 Russian lan.
& lit.
43 Armenian lan.
& lit.
44 Ordo lan. &
lit.
45 Japanese lan.
& lit.
46 Italian lan. &
lit.
47 Spanish lan.
& lit.
48 Chinese lan.
& lit.
Table 3 - University majors’ ranks using AHP
Rank Major Rank Major
1 management 2 mechanical eng.
3 information tech.
eng. 4 industrial eng.
5 medicine 6 film & video
production
7 petroleum eng. 8 law
9 electrical eng. 10 civil eng.
11 biomedical eng. 12 environmental health
13 material eng. 14 urbanization
15 biotechnology 16 chemical eng.
17 oil eng. 18 computer eng.
19 urban forestry eng. 20 historical
construction renovation
21 agriculture eng. 22 historical facility
renovation
23 textile eng. 24 pharmacology
25 mining eng. 26 public health
27 handcraft industry 28 economical sci.
29 cinema 30 veterinarian
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
132
31 architecture 32 architectural eng.
33 aerospace eng. 34 accounting
35 clinical psychology 36 psychology
37 nutrition 38 fashion and textile
design
39 theology 40 industrial design
41 oil exploration eng. 42 herbaceous
production technology
43 animal production
technology 44 theoretical
economics
45 shipping navigation 46 forestry technology
47 environmental
technology 48 Quran sci.
49 Islamic sci. 50 industrial economics
51 polymer eng. 52 dentistry
53 biological sci. 54 insurance
management
55 rural civil eng. 56 nursing
57 general biological
sci. 58 geology
59 journalism 60 railroad eng.
61 professional health 62 fishing technology
63 tore technology 64 Persian lan. & lit.
65 art & cultural
management 66 banking affair
management
67 Islamic
jurisprudence expertise 68 Islamic wisdom
69 political relations 70 political sci.
71 historical arts 72 natural resources
eng.
73 social sci. 74 midwifery
75 law affairs expertise 76 social activism
77 nuclear technology 78 national security
79 photography 80 agricultural
machines technology
81 judiciary sciences 82 robotics eng.
83 chemical sci. 84 medical services
management
85 food industry 86 entomology
87 organizational 88 clerical affairs
Clustering and Ranking University Majors using Data Mining and AHP algorithms
133
psychology
89 emergency medicine 90 social relations sci.
91 political geology 92 physical sci.
93 statistical sci. 94 prosthetics
95 guidance &
consultation 96 family studies
97 graphical design 98 work therapy
99 medicinal plant
production 100 country operations
101 Iranian music 102 universal music
103 physiotherapy 104 speaking therapy
105 audiology 106 clinical laboratory
sci.
107 pilot 108 taxation affairs
109 ECO insurance man. 110 military information
111 philosophy 112 theatre
113 portrait 114 water sci. eng.
115 radiology 116 radiation therapy
117 geography 118 curator
119 postal sci. 120 tourist services
121 museum guidance 122 museum keeping
123 mathematical sci. 124 arsenal
125 cultural sci. 126 physical training &
sports sci.
127 transportation
economics 128 printmaking
129 computer sci. 130 electronic eng.
131 custom affairs 132 marine eng.
133 non-coal mineral
extraction 134 seeing ponder
135 curator in medicine 136 history
137 archaeology 138 wood industry
139 childhood studies 140 Arabic lan. & lit.
141 Arabic translation 142 military sci.
143 training political
teachers 144 dental nursing
145 carpet expertise 146 security sci.
147 English lan. & lit. 148 English translation
149 English training 150 English translation
news expertise
Iranian Journal of Management Sciences, Spring 2010, Vol.5,No.17
134
151 medical documents 152 marine commissar
man.
153 marine business
man. 154 sculpture
155 visual arts 156 aircraft maintenance
eng.
157 aviation 158 marine security
159 technical excavation 160 teeth health
161 anesthesia 162 surgery room
163 aircraft navigation
eng. 164 helicopter pilot
165 teeth prosthetics 166 German lan. & lit.
167 German translation 168 French lan. & lit.
169 French translation 170 Russian lan. & lit.
171 Armenia lan. & lit. 172 Spanish lan. & lit.
173 Chinese lan. & lit. 174 Japanese lan. & lit.
175 Turkish lan. & lit. 176 Italian lan. & lit.
177 Ordo lan. & lit.


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