خوشه بندی و رشته های اصلی رتبه بندی دانشگاه با استفاده از داده کاوی و الگوریتم AHP : یک مطالعه موردی در ایران
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22191||2011||9 صفحه PDF||سفارش دهید||6520 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 1, January 2011, Pages 755–763
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
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 each time more 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 its national future. To manage this and to find which majors are of more important in future, they require a systematic approach to have more deep 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 presence 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 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. By more investing on those majors or providing greater grants for those studying the majors, they intend to motivate more talented students to study these majors. 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 (or 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 then be calculated and evaluated to provide useful decisional information for the government to rationally exploit resources. Among available grouping methods, data mining approaches have been 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 have shown many successes in different applications such as market segmentation, pattern recognition, information retrieval, and so forth ( Cheung, 2003 and Kuo et al., 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 to improve social culture. Armed with this, we apply the analytic hierarchy process (or AHP) as a simple multi-criteria decision making (or MCDM) method for dealing with unstructured, multi-attribute problems. AHP was developed by Saaty, 1980 and Saaty, 1989 and widely studied by other authors (Bolloju, 2001, Kablan, 2004 and Lipovetsky and Conklin, 2002). It consists of breaking down a complex problem into its 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 a best alternative, ranking alternatives as in our case, resource allocation, resolving conflicts, optimization, etc., as well as numerical extensions of AHP (García-Cascales and Lamata, 2009 and Chatzimouratidis and Pilavachi, 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 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 (Porter and Umbach, 2006, Boudarbat, 2008, Berger, 1988 and Crampton et al., 2006). As far as we reviewed, this paper is the first work exploring this problem as a nationwide one, and cluster university majors using a data mining method called k-means. Moreover, university majors are ranked by a MCDM method, called AHP algorithm. The rest of the paper is organized as follows. Section 2 clusters the university majors. Section 3 presents the conceptual model of university majors ranking. Section 4 applies the AHP algorithm to order university majors. Section 5 concludes the paper.
نتیجه گیری انگلیسی
This paper dealt with university majors ranking problem. The 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, they are all eminent though. The 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.