Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

An important goal of scientific data analysis is to understand the behavior of a system or
process based on a sample of the system. In many instances it is possible to observe both …

Multidimensional Projection is a fundamental tool for high-dimensional data analytics and
visualization. With very few exceptions, projection techniques are designed to map data from …

Large observations and simulations in scientific research give rise to high-dimensional data
sets that present many challenges and opportunities in data analysis and visualization …

High-dimensional data, characterized by many features, can be difficult to visualize
effectively. Dimensionality reduction techniques, such as PCA, UMAP, and t-SNE, address …

During the last decades, electronic textual information has become the world's largest and
most important information source. Daily newspapers, books, scientific and governmental …

Persistent Homology (PH) is a method of Topological Data Analysis that analyzes the
topological structure of data to help data scientists infer relationships in the data to assist in …

Sets of multiple scalar fields can be used to model many types of variation in data, such as
uncertainty in measurements and simulations or time‐dependent behavior of scalar …

Methoden und Techniken zur automatischen Verarbeitung und inhaltlichen Erfassung
großer Mengen an Textdokumenten haben in den vergangenen Jahren enorm an …

The Gaussian mixture model (GMM) describes the distribution of random variables from
several different populations. GMMs have widespread applications in probability theory …