Exploratory data analysis (EDA) has emerged as a critical tool for users to gain deep
insights into data and unearth hidden patterns. The integration of recommendation …

Temporal evolving set data are time-varying and growing ubiquitous in person re-
identification, parameter choice, and streaming data analysis. We construct a workflow to …

Dimensionality reduction techniques are widely used for visualizing high-dimensional data.
However, support for interpreting patterns of dimension reduction results in the context of the …

We present an approach that shows all relevant subspaces of categorical data condensed in
a single picture. We model the categorical values of the attributes as co-occurrences with …

The application of dimensionality reduction (DR) in effectively handling high-dimensional
data is becoming increasingly prominent. However, mainstream methods in projection …

Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional
data. One main aim of DR is to reveal hidden patterns that lie on intrinsic low-dimensional …

Despite exploratory data analysis (EDA) is a powerful approach for uncovering insights from
unfamiliar datasets, existing EDA tools face challenges in assisting users to assess the …

Acoustic quality detection is vital in the manufactured products quality control field since it
represents the conditions of machines or products. Recent work employed machine learning …

We introduce Projection Ensemble, a novel approach for identifying and visualizing robust
structures across multidimensional projections. Although multidimensional projections, such …

Dimensionality reduction (DR) techniques are essential for visually analyzing high-
dimensional data. However, visual analytics using DR often face unreliability, stemming from …