The volumes of data that are collected and analyzed in all aspects of life rise steadily and offer numerous challenges and opportunities. When analyzing large volumes of data, two main considerations are the capability to detect relationships within the data and the time efficiency necessary when processing the data. Large data sets in particular often contain dependencies that are less obvious than linear or proportional relationships. Such complex dependencies could be quantified and analyzed with methods from the field of information theory. In this dissertation, we study time-efficient methods to detect, quantify and illustrate complex dependencies.