Enabling decision trend analysis with interactive scatter plot matrices visualization. (April 2016)

• Record Type: Journal Article
• Authors: Wang, Wen Bo; Huang, Mao Lin; Nguyen, Quang Vinh; Huang, Weidong; Zhang, Kang; Huang, Tze-Haw
• Is Part Of: Journal of visual languages & computing. Volume 33(2016)
• Page Start: 13
• Page End: 23
• Publication Date: 2016-04
• Subjects: Rough set theory -- Dimensionality reduction -- Scatter plot matrices -- Parrallel coordinate geomery -- Visual data analytics -- Visual decision making; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2015.11.002
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: This paper presents a new interactive scatter plot visualization for multi-dimensional data analysis. We apply Rough Set Theory (RST) to reduce the visual complexity through dimensionality reduction. We use an innovative point-to-region mouse click concept to enable direct interactions with scatter points that are theoretically impossible. To show the decision trend we use a virtual Z dimension to display a set of linear flows showing approximation of the decision trend. We conducted case studies to demonstrate the effectiveness and usefulness of our new technique for analyzing the property of three popular data sets including wine quality, wages and cars. The paper also includes a pilot usability study to evaluate parallel coordinate visualization with scatter plot matrices visualization with RST results.