Visualization of big high dimensional data in a three dimensional space

Ying Xie; Pooja Chenna; Linh Le; Jing He

doi:10.1145/3006299.3006340

Visualization of big high dimensional data in a three dimensional space

Ying Xie, Pooja Chenna, Linh Le, Jing He

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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.

Original language	American English
Title of host publication	The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
Publisher	Association for Computing Machinery, Inc
Pages	61-66
Number of pages	6
ISBN (Electronic)	9781450346177
DOIs	https://doi.org/10.1145/3006299.3006340
State	Published - 2016
Event	3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016 - Shanghai, China Duration: Dec 6 2016 → Dec 9 2016

Publication series

Name	Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016

Conference

Conference	3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016
Country/Territory	China
City	Shanghai
Period	12/6/16 → 12/9/16

Keywords

Big data visualization
Big high dimensional data
Dimension reduction
Optimal clustering structure
Visualizability
Visualize high-dimensional data

Access to Document

10.1145/3006299.3006340

https://dl.acm.org/citation.cfm?doid=3006299.3006340

Cite this

Xie, Y., Chenna, P., Le, L., & He, J. (2016). Visualization of big high dimensional data in a three dimensional space. In The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (pp. 61-66). (Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016). Association for Computing Machinery, Inc. https://doi.org/10.1145/3006299.3006340

Xie, Ying ; Chenna, Pooja ; Le, Linh et al. / Visualization of big high dimensional data in a three dimensional space. The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. Association for Computing Machinery, Inc, 2016. pp. 61-66 (Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016).

@inproceedings{d691b4e1b5ab4a2d874c91a5a279727d,

title = "Visualization of big high dimensional data in a three dimensional space",

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.",

keywords = "Big data visualization, Big high dimensional data, Dimension reduction, Optimal clustering structure, Visualizability, Visualize high-dimensional data",

author = "Ying Xie and Pooja Chenna and Linh Le and Jing He",

note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

year = "2016",

doi = "10.1145/3006299.3006340",

language = "American English",

series = "Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016",

publisher = "Association for Computing Machinery, Inc",

pages = "61--66",

booktitle = "The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies",

}

Xie, Y, Chenna, P, Le, L & He, J 2016, Visualization of big high dimensional data in a three dimensional space. in The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016, Association for Computing Machinery, Inc, pp. 61-66, 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016, Shanghai, China, 12/6/16. https://doi.org/10.1145/3006299.3006340

Visualization of big high dimensional data in a three dimensional space. / Xie, Ying; Chenna, Pooja; Le, Linh et al.
The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. Association for Computing Machinery, Inc, 2016. p. 61-66 (Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Visualization of big high dimensional data in a three dimensional space

AU - Xie, Ying

AU - Chenna, Pooja

AU - Le, Linh

AU - He, Jing

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Big data visualization

KW - Big high dimensional data

KW - Dimension reduction

KW - Optimal clustering structure

KW - Visualizability

KW - Visualize high-dimensional data

UR - https://www.scopus.com/pages/publications/85013173804

U2 - 10.1145/3006299.3006340

DO - 10.1145/3006299.3006340

M3 - Conference contribution

T3 - Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016

SP - 61

EP - 66

BT - The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

PB - Association for Computing Machinery, Inc

T2 - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016

Y2 - 6 December 2016 through 9 December 2016

ER -

Xie Y, Chenna P, Le L, He J. Visualization of big high dimensional data in a three dimensional space. In The 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. Association for Computing Machinery, Inc. 2016. p. 61-66. (Proceedings - 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2016). doi: 10.1145/3006299.3006340

Visualization of big high dimensional data in a three dimensional space

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

LexisNexis and HPCC Systems Lab Endowment Scholarship

Cite this

Visualization of big high dimensional data in a three dimensional space

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

LexisNexis and HPCC Systems Lab Endowment Scholarship

Cite this