En este artÍculo se presenta una fundamentacion teórica de las principales medidas de similaridad basadas en compresión de datos, estas técnicas surgieron en la última década y han presentado gran utilidad en diversos campos de las ciencias, implementación de técnicas de agrupamiento u otras máquinas de aprendizaje pueden hacer una clasificación entre objetos.In this paper a theoretical foundation of main similarity measures based on data compression is presented. These approaches are used to implement different learning machines for classification between different objects. Many scientific fields have been positively impacted with these similarity measures of information, among of them are time series, images, DNA, video, audio, software metrics. The theoretical basis of these compression techniques is the Kolmogorov complexity. This paper defines key concepts of this complexity and analogies to Shannon information theory. We also present the applications of these techniques in clustering algorithms for classification of electrocardiographic signals.