Icon-based Visualization of Large High-Dimensional Datasets

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Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Ping Chen, Chenyi Hu, Wei Ding, Heloise Lynn, Yves Simon, "Icon-based Visualization of Large High-Dimensional Datasets," Data Mining, IEEE International Conference on, pp. 505, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250963, author = {Ping Chen and Chenyi Hu and Wei Ding and Heloise Lynn and Yves Simon}, title = {Icon-based Visualization of Large High-Dimensional Datasets}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {505}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250963}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Icon-based Visualization of Large High-Dimensional Datasets SN - 0-7695-1978-4 SP EP A1 - Ping Chen, A1 - Chenyi Hu, A1 - Wei Ding, A1 - Heloise Lynn, A1 - Yves Simon, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Ping Chen, Univ. of Houston-Downtown, TX

Chenyi Hu, Univ. of Central Arkansas, AR

Wei Ding, Univ. of Houston-Clear Lake, TX

Heloise Lynn, Lynn Inc., Houston, TX

Yves Simon, Lynn Inc., Houston, TX

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250963

High dimensional data visualization is critical to data analysts since it gives a direct view of original data. We present a method to visualize large amount of high dimensional data. We divide dimensions of data into several groups. Then, we use one icon to represent each group, and associate visual properties of each icon with dimensions in each group. A high dimensional data record will be represented by multiple different types of icons located in the same position. Furthermore, we use summary icons to display local details of viewer's interests and the whole data set at meantime. We show its effectiveness and efficiency through a case study on a real large data set.

Citation:

Ping Chen, Chenyi Hu, Wei Ding, Heloise Lynn, Yves Simon, "Icon-based Visualization of Large High-Dimensional Datasets," icdm, pp.505, Third IEEE International Conference on Data Mining (ICDM'03), 2003

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