Clustering and ranking university majors using data mining and AHP algorithms: A case study in Iran

Authors:
A. Rad;B. Naderi;M. Soltani
Affiliations:
Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran;Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran;Department of Management (EMBA), Islamic Azad University of Bonab, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 9
Cited 3

Integration of self-organizing feature map and K-means algorithm for market segmentation

Computers and Operations Research
On distributing the clustering process

Pattern Recognition Letters
Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce

Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
Methodologies, tools and languages for building ontologies: where is their meeting point?

Data & Knowledge Engineering
k-means: a new generalized k-means clustering algorithm

Pattern Recognition Letters
Mining customer knowledge for product line and brand extension in retailing

Expert Systems with Applications: An International Journal
Application of Monte Carlo AHP in ranking dental quality attributes

Expert Systems with Applications: An International Journal
A case study of applying data mining techniques in an outfitter's customer value analysis

Expert Systems with Applications: An International Journal
Selection of a cleaning system for engine maintenance based on the analytic hierarchy process

Computers and Industrial Engineering

A two-grade approach to ranking interval data

Knowledge-Based Systems
Note on a new optimization based approach for estimating priority weights and related consistency index

Expert Systems with Applications: An International Journal
Review: Educational data mining: A survey and a data mining-based analysis of recent works

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Although all university majors are prominent, and the necessity of their presence 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 the time goes by; that is, different majors are desirable at different time. If the government is informed of which majors could tackle today existing problems of world and its country, it surely more esteems those majors. 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.