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• W2551427248 abstract "Information visualization is essential for improving effectiveness and efficiency of data exploration and knowledge discovery. Therefore, visualization has been used in a wide range of fields from biology, medicine, criminal activity analysis to business and education. Information visualization has become more important than ever as the amount of data being generated has increased dramatically in recent years. One of the major difficulties of information visualization is performance, and this is even more critical when visualizing big data. One potential solution to this challenge is data sampling while maintaining fidelity of visual representation. In this paper, we propose two new centrality clustering-based sampling approaches that apply centrality measures on clusters of data points in order to make more informed sampling than random sampling approaches. We evaluate the new methods on graph data sets. The results show that the new methods significantly outperform existing data sampling methods in term of perceived differences and their ability to preserve essential visual information. Moreover computational complexity is comparable or even better than simple random sampling methods." @default.
• W2551427248 created "2016-11-30" @default.
• W2551427248 creator A5072242525 @default.
• W2551427248 creator A5088890462 @default.
• W2551427248 date "2016-07-01" @default.
• W2551427248 modified "2023-09-29" @default.
• W2551427248 title "Centrality clustering-based sampling for big data visualization" @default.
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• W2551427248 doi "https://doi.org/10.1109/ijcnn.2016.7727433" @default.
• W2551427248 hasPublicationYear "2016" @default.
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