mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations
This addresses the challenge of handling large datasets for data visualization, which is incremental as it builds on existing optimization methods.
The paper tackled the problem of visualizing large datasets by proposing mQAPViz, a divide-and-conquer multi-objective optimization algorithm that scales to millions of data objects, showing it is a competitive alternative to existing techniques.
Algorithms for data visualizations are essential tools for transforming data into useful narratives. Unfortunately, very few visualization algorithms can handle the large datasets of many real-world scenarios. In this study, we address the visualization of these datasets as a Multi-Objective Optimization Problem. We propose mQAPViz, a divide-and-conquer multi-objective optimization algorithm to compute large-scale data visualizations. Our method employs the Multi-Objective Quadratic Assignment Problem (mQAP) as the mathematical foundation to solve the visualization task at hand. The algorithm applies advanced sampling techniques originating from the field of machine learning and efficient data structures to scale to millions of data objects. The algorithm allocates objects onto a 2D grid layout. Experimental results on real-world and large datasets demonstrate that mQAPViz is a competitive alternative to existing techniques.