Abstract
The k-nearest neighbours algorithm is one of the most widely used data mining models because of its simplicity and accurate results. However, when it comes to deal with big datasets, with potentially noisy and missing information, this technique becomes ineffective and inefficient. Due to its drawbacks to tackle large amounts of imperfect data, plenty of research has aimed at improving this algorithm by means of data preprocessing techniques. These weaknesses have turned out as strengths and the k-nearest neighbours rule has become a core model to actually detect and correct imperfect data, eliminating noisy and redundant data, as well as correcting missing values.
In this work, we delve and review the role of the k nearest neighbour algorithm can play to come up with smart data from big datasets. We analyse how this model is affected by the big data problem, but at the same time, how it can be used to transform raw data into useful data. Concretely, we discuss the benefits of recent big data technologies (Hadoop and Spark) to enable this model to address large amounts of data, as well as the usefulness of prototype reduction and missing values imputation techniques based on it. As a result, guidelines on the use of the k-nearest neighbour to obtain Smart data are provided and new potential research trends are discussed.