A Novel Triangulate Mapping Based on Self- Organized Anchor Points for Data Visualization
Without a form of visual feedback, multivariate data would be reduced to a lump of numbers that very few people would be able to appreciate and be benefited from. This research paper proposes a novel triangulate mapping technique based on selforganizing anchor points for multivariate data visualizat...
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| Main Authors: | , |
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| Format: | E-Article |
| Published: |
American Scientific Publishers
2017
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/18827/ http://www.aspbs.com/science.htm |
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| Institution: | Universiti Malaysia Sarawak |
| Summary: | Without a form of visual feedback, multivariate data would be reduced to a lump of numbers that very few people would be able to appreciate and be benefited from. This research paper proposes a novel triangulate mapping technique based on selforganizing anchor points for multivariate data visualization. Self-Organizing Map (SOM) and a modified Adaptive Coordinates (AC) are hybridized to produce the anchor points in the 2D space. The trained anchor points are used to triangulate data onto a topologically preserved 2D space. The empirical studies that produce topologically preserved data visualizations for high dimension and arbitrarily shaped clusters in simulated, benchmarking, and real-life dataset show its usefulness in providing intuitive visual feedback to the user. |
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