- Title
- mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations
- Creator
- Sanhueza, Claudio; Jiménez, Francia; Berretta, Regina; Moscato, Pablo
- Relation
- Genetic and Evolutionary Computation Conference (GECCO'18). GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference (Kyoto, Japan 15-19 July, 2018) p. 737-744
- Publisher Link
- http://dx.doi.org/10.1145/3205455.3205457
- Publisher
- Association for Computing Machinery
- Resource Type
- conference paper
- Date
- 2018
- Description
- 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 M Q A P V I Z, 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 M Q A P V I Z is a competitive alternative to existing techniques.
- Subject
- multi-objective optimization; visualization; large datasets
- Identifier
- http://hdl.handle.net/1959.13/1401757
- Identifier
- uon:34958
- Identifier
- ISBN:9781450356183
- Rights
- © Owner/Author 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference, http://dx.doi.org/10.1145/3205455.3205457
- Language
- eng
- Full Text
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