In this paper, we review the feature selection problem in mining issues. The application of soft computing techniques to data mining and knowledge discovery is now emerging in order to enhance the effectiveness of the traditional classification methods coming from machine learning. A survey of the approaches presented in the literature to select relevant features by using genetic algorithms is given. The different values of the genetic parameters utilized as well as the fitness functions are compared. A more detailed review of the proposals in the mining fields of databases, text and the Web is also given.

The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the strengths and weaknesses of using association rules for solving different tasks in textual social media. Finally, the paper provides a perspective overview of the challenges that association rules must face in the next decade within the field of social media mining.

Non-residential buildings are responsible for more than a third of global energy consumption. Estimating building energy consumption is the first step towards identifying inefficiencies and optimizing energy management policies. This paper presents a study of Deep Learning techniques for time series analysis applied to building energy prediction with real environments. We collected multisource sensor data from an actual office building under normal operating conditions, pre-processed them, and performed a comprehensive evaluation of the accuracy of feed-forward and recurrent neural networks to predict energy consumption. The results show that memory-based architectures (LSTMs) perform better than stateless ones (MLPs) even without data aggregation (CNNs), although the lack of ample usable data in this type of problem avoids making the most of recent techniques such as sequence-to-sequence (Seq2Seq). Graphical