A mixed integer linear model for clustering with variable selection
Computers and Operations Research
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The Academic Journal Ranking Problem consists in formulating a formal assessment of scientific journals. An outcome variable must be constructed that allows valid journal comparison, either as a set of tiers (ordered classes) or as a numerical index. But part of the problem is also to devise a procedure to get this outcome, that is, how to get and use relevant data coming from expert opinions or from citations database. We propose a novel approach to the problem that applies fuzzy cluster analysis to peer reviews and opinion surveys. The procedure is composed of two steps: the first is to collect the most relevant qualitative assessments from international organizations (for example, the ones available in the Harzing database) and, as inductive analysis, to apply fuzzy clustering to determine homogeneous journal classes; the second deductive step is to determine the hidden logical rules that underlies the classification, using a classification tree to reproduce the same patterns of the first step. Our approach is applied to the classification of 138 academic journals that were selected by members of AMASES, an Italian mathematics association, as the most prominent journals of our field. The clusters that are determined by our method show that rankings are affected by two hidden dimensions: one is the academic prestige of a publication, but the other is the disciplinary diffusion of a mathematics subfield. In particular, mathematics journals that are close to finance or economics are usually ranked better than journals dealing with linear algebra or systems dynamics.