VISAnt: Unsupervised Data Exploration with Chernoff Faces

Ivaná Sixtova, Ladislav Peska, Jakub Lokoc, David Bernhauer, Tomás Skopal

Abstract: In the era of big data, there is a growing need to explore large, high-dimensional datasets in an unsupervised manner to identify clusters, detect outliers, and generate labels. Many existing tools and methods facilitate these tasks through visual exploration, but there remains a significant gap between the ‘zoomed-in’ view, which focuses on individual objects using tables and bar plots, and the ‘zoomed-out’ view, where the entire dataset is visualized in two dimensions using dimensionality reduction techniques like PCA or t-SNE.

External IDs:dblp:conf/sisap/SixtovaPLBS25