mQAPViz
| Autor: | Regina Berretta, Pablo Moscato, Francia Jiménez, Claudio Sanhueza |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Divide and conquer algorithms Optimization problem Computer science business.industry Quadratic assignment problem Computer Science - Human-Computer Interaction Computer Science - Neural and Evolutionary Computing 02 engineering and technology Grid computer.software_genre Data structure Multi-objective optimization Field (computer science) Human-Computer Interaction (cs.HC) Visualization Data visualization 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Neural and Evolutionary Computing (cs.NE) Data mining business computer |
| Zdroj: | GECCO |
| DOI: | 10.1145/3205455.3205457 |
| Popis: | 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. Proceeding GECCO 18 Proceedings of the Genetic and Evolutionary Computation Conference. Pages 737-744 Kyoto, Japan - July 15 - 19, 2018 |
| Databáze: | OpenAIRE |
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