The evaluation of quality in higher education is today a matter of great 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.

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Recommendation systems are generally complicated, due they search to increase their reach and robustness, they combine different artificial intelligence approaches mainly of supervised learning. A disadvantage of this type of systems is that they must have a prior classification to be able to train a system and after they can be able to make decisions in a simmilar way that a human would do it; however, the task of classification is often expensive because is needed to consult with experts the possible classification (also known as label) that should be given to a specific data; although this method can be profitable for large companies, it is not for small and medium companies. This is the reason which the present work shows a proposal of a simple system that does not need to have a previous classification, allowing it to be profitable for small and medium enterprises in decision making.

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Currently, with the massification of electronic media such as cell phones and computers, computer security is more in force than ever before; nevertheless, the level of culture in the matter of the users is far from being the ideal. The present work shows a study conducted with two different types of computer attacks, one remotely and the anther one connecting a physical device to a computer. Subsequently, the execution and validation of the proposal in a social environment is shown, finally the conclusions obtained are shown.