Abstract
Data visualization is a powerful tool and widely adopted by organizations for its effectiveness to abstract the right information, understand, and interpret results clearly and easily. The real challenge in any data science exploration is to visualize it. Visualizing a discrete, categorical data attribute using bar plots, pie charts are a few of the effective ways for data exploration. Most of the datasets have a large number of features. In other words, data is distributed across a high number of dimensions. Visually exploring such high-dimensional data can then become challenging and even practically impossible to do manually. Hence it is essential to understand how to visualize high-dimensional datasets. t-Distributed stochastic neighbor embedding (t-SNE) is a technique for dimensionality reduction and explicitly applicable to the visualization of high-dimensional datasets.
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Soni, J., Prabakar, N., Upadhyay, H. (2020). Visualizing High-Dimensional Data Using t-Distributed Stochastic Neighbor Embedding Algorithm. In: Arabnia, H.R., Daimi, K., Stahlbock, R., Soviany, C., Heilig, L., Brüssau, K. (eds) Principles of Data Science. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-43981-1_9
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