Algorithms and software for data mining and machine learning: a critical comparative view from a systematic review of the literature

Today, a greater generation of information is produced as a consequence of the technological development of society. The Internet has facilitated the access and extraction of this information, thus pursuing the automatic discovery of the knowledge contained within. In this context, data mining aims to discover patterns, profiles and trends of a large volume of data, for which multiple learning techniques are available. The selection of which technique to use depends on the type of result desired to obtain and the data that are available, considering that the algorithms for these tasks date mostly from the early twentieth century and are now the basis of these new technologies. The aim of this study is to show the development of these techniques in the field of scientific research and to present the evolution of algorithms and software for data mining in recent years. To this end, the systematic literature review methodology was applied, as it is considered a systematic process that identifies, evaluates, and interprets the work of researchers in a chosen field. As a result, we present a comparative analysis of the most outstanding software: Alteryx, TIBCO Data Science, RapidMiner and WEKA, their capacities for data mining processes and a description of the algorithms and techniques of machine learning that are currently on the rise.