Multivariate Data Analysis

The analysis of multivariate data is key in many research and development endeavors conducted at GRIS. Related fields of research involve Data Mining, Machine Learning, as well as visual-interactive approaches like Information Visualization and Visual Analytics.

Relevant analysis tasks of data scientists include obtaining an overview of large multivariate data collections, aiming for the identification of structural aspects like clusters or outliers. In the same way, analysts are interested in relations in the data, such as correlations between individual attributes of the multivariate data sets.

Multivariate Data Analysis explained

Multivariate data is one of the most important and widespread types of data. In many cases multivariate data are converted/transformed into feature vectors which can then be applied in data mining, machine learning, and information retrieval algorithms. Depending on the analysis task and the amount of information given in advance, algorithms for the unsupervised, semi-supervised, or supervised analysis of multivariate data can be applied.

With the growing relevance of visualization and interactive interfaces in todays’ data science workflows, Information visualization and visual analytics play an important role in the analysis of multivariate data.