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A Novel Triangulate Mapping Based on Self-Organized Anchor Points for Data Visualization

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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 self-organizing 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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Keywords: Self-Organizing Map; Topologically Preserved Visualization; Triangulate Mapping

Document Type: Research Article

Affiliations: Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia

Publication date: November 1, 2017

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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