Abstract
Data visualization is used to extract insight from large datasets. Data scientists repeatedly keep generating different visualizations from the datasets for their hypothesis. Analyzing datasets which has many attributes could be a cumbersome process and lead to errors. The goal of this research paper is to automatically recommend interesting visualization patterns using optimized datasets from different databases. It reduces the time spent on low utility visualizations and displays recommended patterns.
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Muniswamaiah, M., Agerwala, T., Tappert, C.C. (2020). Automatic Visual Recommendation for Data Science and Analytics. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_11
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DOI: https://doi.org/10.1007/978-3-030-39442-4_11
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