Context: In recent years, the valuable knowledge that can be retrieved from petabyte scale
datasets–known as Big Data–led to the development of solutions to process information …

For an investigative journalist, a large collection of documents obtained from a Freedom of
Information Act request or a leak is both a blessing and a curse: such material may contain …

Deep neural networks are now rivaling human accuracy in several pattern recognition
problems. Compared to traditional classifiers, where features are handcrafted, neural …

Labeling data instances is an important task in machine learning and visual analytics. Both
fields provide a broad set of labeling strategies, whereby machine learning (and in particular …

Designing a complete visualization system involves many subtle decisions. When designing
a complex, real-world visualization system, such decisions involve many types of …

The problem of projecting multidimensional data into lower dimensions has been pursued
by many researchers due to its potential application to data analyses of various kinds. This …

In recent years, dimensionality‐reduction techniques have been developed and are widely
used for hypothesis generation in Exploratory Data Analysis. However, these techniques are …

Performing exhaustive searches over a large number of text documents can be tedious,
since it is very hard to formulate search queries or define filter criteria that capture an …

We propose a new graph layout method based on a modification of the t‐distributed
Stochastic Neighbor Embedding (t‐SNE) dimensionality reduction technique. Although t …

Recently, strategies for developing colorimetric sensors relying on nanozymes for the
detection of analytes of health, food, and environmental interest have gained significant …