Parallel rough set: dimensionality reduction and feature discovery of multi-dimensional data in visualization

  • Authors:

  • Tze-Haw Huang;Mao Lin Huang;Jesse S. Jin

  • Affiliations:

  • School of Software, University of Technology Sydney, Sydney, Australia;School of Software, University of Technology Sydney, Sydney, Australia;School of Design, Communication and Information Technology, University of Newcastle, Newcastle, Australia

  • Venue:
  • ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Attempt to visualize high dimensional datasets typically encounter over plotting and decline in visual comprehension that makes the knowledge discovery and feature subset analysis difficult. Hence, reshaping the datasets using dimensionality reduction technique is paramount by removing the superfluous attributes to improve visual analytics. In this work, we applied rough set theory as dimensionality reduction and feature selection methods on visualization to facilitate knowledge discovery of multi-dimensional datasets. We provided the case study using real datasets and comparison against other methods to demonstrate the effectiveness of our approach.