360° Rays: Familiar, Yet Different and Better!

Authors

Bipasha Garg (International Institute of Information Technology, Hyderabad), Kamalakar Karlapalem (International Institute of Information Technology)

Presentation

Session: Form Follows Function
Time: Tuesday, Nov 10, 10:36 – 10:45 (US/Eastern) · session 10:00 – 11:30
Location: Hall Essex north

Keywords

Data visualisation, Data analytics

Abstract

Visualising all dimensions of multi-dimensional real data while providing semantics is a challenging problem in data analysis. In this paper, we present the visualisation of multi-dimensional real data using our tool, 360° RAYS. RAYS plots all features and points of the dataset and provides information such as the geometric positioning of points or classes in the orthants of space, thereby aiding understanding of the closeness of points, classification of points, and ordering of subspaces. This visualisation technique is built after exploring viable options to plot multi-dimensional data, which included parallel coordinates, concentric coordinates, and circle segments, among others. The key idea of our approach is a hierarchical concentric ring structure, where each successive ring incrementally adds a dimension to the existing subspace, ordered by the increasing or decreasing coefficient of variation of data in the added dimension. Normalised data points are mapped to ring sectors based on a bitwise encoding of their coordinate values (non-negative or negative). The advantage of RAYS is that it presents the complete data with all dimensions and provides a clear visual grouping of points by subspace concentration. The visualisation also shows pure and overlapping orthants within the data in a flattened 2D radial layout.

For Practitioners

RAYS is intended for practitioners who analyze and interpret multi-dimensional datasets through visual analytics. This includes data scientists and machine learning engineers evaluating clustering outcomes, business intelligence analysts exploring multidimensional data for decision support, and developers of visual analytics platforms seeking scalable and interactive visualization techniques. The framework is also valuable for domain experts in areas such as healthcare, finance, cybersecurity, geospatial analytics, and scientific research, where understanding cluster structure, identifying patterns, and detecting anomalies are essential for informed decision-making.