As more and more data becomes available through a variety of sources, for example, the Internet of Things, the need for analyzing high-dimensional data increases permanently. Visual analysis of multi-dimensional data offers an intuitive approach to better understand datasets, as humans are generally good at reasoning over spatial objects. To access a better comprehension of a dataset, data analysts often use visualization techniques in the data exploration phase. Especially convenient are dimensionality reduction techniques. They can be used for visualizations that depict the structure of the dataset and the relationship of the high-dimensional datapoints. However, visualizations based on dimensionality reduction often suffer from limited interpretability of the low-dimensional map. This is because the map does not show the relationship the low-dimensional datapoints have with the high-dimensional attributes. The Data Context Map algorithm proposed by Cheng and Mueller solves this issue by providing a low-dimensional map that accounts for the datapoint relationship, the attribute relationship, and the datapoint-attribute relationship. In this thesis, we present an implementation of the Data Context Map algorithm, enhanced by an interaction technique called Probing that allows to further reduce the error of the low-dimensional map locally. This implementation makes it possible to create visualizations of high-dimensional datasets and its attributes that improve the interpretation and the understanding of the data. Decision-making processes can be enhanced as high-dimensional data can be better analyzed. We apply the augmented Data Context Map to a variety of real-world datasets to show the advanced interpretability of the low-dimensional map.