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Discretization of numerical data is one of the most influential data preprocessing tasks in knowledge discovery and data mining. The purpose of attribute discretization is to find concise data representations as categories which are adequate for the learning task retaining as much information in the original continuous attribute as possible. In this article, we present an updated overview of discretization techniques in conjunction with a complete taxonomy of the leading discretizers. Despite the great impact of discretization as data preprocessing technique, few elementary approaches have been developed in the literature for Big Data. The purpose of this article is twofold: a comprehensive taxonomy of discretization techniques to help the practitioners in the use of the algorithms is presented; the article aims is to demonstrate that standard discretization methods can be parallelized in Big Data platforms such as Apache Spark, boosting both performance and accuracy. We thus propose a distributed implementation of one of the most well-known discretizers based on Information Theory, obtaining better results than the one produced by: the entropy minimization discretizer proposed by Fayyad and Irani. Our scheme goes beyond a simple parallelization and it is intended to be the first to face the Big Data challenge. WIREs Data Mining Knowl Discov 2016, 6:5-21. doi: 10.1002/widm.1173 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

The term 'Big Data' has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme and Hadoop, its open-source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms. WIREs Data Mining Knowl Discov 2014, 4:380-409. doi: 10.1002/widm.1134 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning. In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will focus on the suitability and potential of the search performed by EAs in the development of SD algorithms. Future directions in the use of EAs for SD are also presented in order to show the advantages and benefits that this search strategy contribute to this task. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining technique. The main drawback of this technique appears when massive amounts of data—likely to contain noise and imperfections—are involved, turning this algorithm into an imprecise and especially inefficient technique. These disadvantages have been subject of research for many years, and among others approaches, data preprocessing techniques such as instance reduction or missing values imputation have targeted these weaknesses. As a result, these issues have turned out as strengths and the k‐nearest neighbors rule has become a core algorithm to identify and correct imperfect data, removing noisy and redundant samples, or imputing missing values, transforming Big Data into Smart Data—which is data of sufficient quality to expect a good outcome from any data mining algorithm. The role of this smart data gleaning algorithm in a supervised learning context are investigated. This includes a brief overview of Smart Data, current and future trends for the k‐nearest neighbor algorithm in the Big Data context, and the existing data preprocessing techniques based on this algorithm. We present the emerging big data‐ready versions of these algorithms and develop some new methods to cope with Big Data. We carry out a thorough experimental analysis in a series of big datasets that provide guidelines as to how to use the k‐nearest neighbor algorithm to obtain Smart/Quality Data for a high‐quality data mining process. Moreover, multiple Spark Packages have been developed including all the Smart Data algorithms analyzed. This article is categorized under:Technologies > Data PreprocessingFundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Classification The k‐nearest neighbors algorithm plays a key role to cope with Big Data by transforming it into Smart data that is free of redundant information, noise and/or missing values. Gleaning quality data is essential for a correct data mining process that will uncover valuable insights. [ABSTRACT FROM AUTHOR]