Abstract
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.