NDLI: Visualization of big high dimensional data in a three dimensional space

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Visualization of big high dimensional data in a three dimensional space

Content Provider	ACM Digital Library
Author	Xie, Ying Chenna, Pooja Planteen, Jacey Le, Linh He, Jing Selena
Abstract	This paper studies feasibility and scalable computing processes for visualizing big high dimensional data in a 3 dimensional space by using dimension reduction techniques. More specifically, we propose an unsupervised approach to compute a measure that is called visualizability in a 3 dimensional space for a high dimensional data. This measure of visualizability is computed based on the comparison of the clustering structures of the data before and after dimension reduction. The computation of visualizability requires finding an optimal clustering structure for the given data sets. Therefore, we further implement a scalable approach based on K-Means algorithm for finding an optimal clustering structure for the given big data. Then we can reduce the volume of a given big data for dimension reduction and visualization by sampling the big data based on the discovered clustering structure of the data.
Starting Page	61
Ending Page	66
Page Count	6
File Format	PDF
ISBN	9781450346177
DOI	10.1145/3006299.3006340
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-12-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Big high dimensional data Dimension reduction Visualizability Optimal clustering structure Visualize high-dimensional data Big data visualization
Content Type	Text
Resource Type	Article

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