*Article* **A Comparative Ranking Model among Mexican Universities Using Pattern Recognition**

**Daniel Edahi Urueta 1,\*, Pedro Lara 2, Miguel Ánge<sup>l</sup> Gutiérrez 2, Sergio Gerardo de-los-Cobos 2, Eric Alfredo Rincón 3 and Román Anselmo Mora 3**


**Abstract:** The evaluation of quality in higher education is today a matter of grea<sup>t</sup> importance in most countries because the allocation of resources should be in accordance with the quality of universities. Due to this, there are numerous initiatives to create instruments and evaluation tools that can offer a quality comparison among institutions and countries, the results of these efforts used to be called international rankings. These rankings include some that are "reputational" or subjective, based on opinion polls applied to groups that, which is estimated, can issue authorized views. There are also "objective" rankings, based on performance indicators, which are calculated from a certain set of empirical data; however, on many occasions these indicators are sponsored by universities with the desire to appear among the best universities and emphasize some characteristics more than others, which makes them untrustworthy and very variable between each other. In this sense, we considered the Comparative Study of Mexican Universities (CSMU), a database of statistical information on education and research of Mexican higher education institutions, this database allows users to be responsible for establishing comparisons and relationships that may exist among existing information items, or building indicators based on their own needs and analysis perspectives (Márquez, 2010). This work develops an unsupervised alternative model of ranking among universities using pattern recognition, specifically clustering techniques, which are based on public access data. The results of the CSMU database are obtained by analyzing 60 universities as a first iteration, but to present the final results UNAM is excluded.

**Keywords:** university ranking; unsupervised pattern recognition; clustering techniques
