Research Article
Visualization of Mixed Attributed High-Dimensional Dataset Using Singular Value Decomposition
@INPROCEEDINGS{10.1007/978-3-319-58967-1_1, author={Bindiya Varghese and A. Unnikrishnan and K. Poulose Jacob}, title={Visualization of Mixed Attributed High-Dimensional Dataset Using Singular Value Decomposition}, proceedings={Big Data Technologies and Applications. 7th International Conference, BDTA 2016, Seoul, South Korea, November 17--18, 2016, Proceedings}, proceedings_a={BDTA}, year={2017}, month={6}, keywords={Data visualization Mixed attribute datasets Dimensionality reduction SVD}, doi={10.1007/978-3-319-58967-1_1} }- Bindiya Varghese
A. Unnikrishnan
K. Poulose Jacob
Year: 2017
Visualization of Mixed Attributed High-Dimensional Dataset Using Singular Value Decomposition
BDTA
Springer
DOI: 10.1007/978-3-319-58967-1_1
Abstract
The ability to present data or information in a pictorial format makes data visualization, one of the major requirement in all data mining efforts. A thorough study of techniques, which presents visualization, it was observed that many of the described techniques are dependent on data and the visualization needs support specific to domain. On contrary, the methods based on Eigen decomposition, for elements in a higher dimensional space give meaningful depiction. The illustration of the mixed attribute data and categorical data finally signifies the data set a point in higher dimensional space, the methods of singular value decomposition were applied for demonstration in reduced dimensions (2 and 3). The data set is then projected to lower dimensions, using the prominent singular values. The proposed methods are tested with datasets from UCI Repository and compared.