Exploring linear projections for revealing clusters, outliers, and trends in subsets of multi-dimensional datasets. (October 2018)

• Record Type: Journal Article
• Authors: Xia, Jiazhi; Gao, Le; Kong, Kezhi; Zhao, Ying; Chen, Yi; Kui, Xiaoyan; Liang, Yixiong
• Is Part Of: Journal of visual languages & computing. Volume 48(2018)
• Page Start: 52
• Page End: 60
• Publication Date: 2018-10
• Subjects: Multi-dimensional data -- Projection -- Visual exploring; 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.2018.08.003
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: Identifying patterns in 2D linear projections is important in understanding multi-dimensional datasets. However, local patterns, which are composed of partial data points, are usually obscured by noises and missed in traditional quality measure approaches that measure the whole dataset. In this paper, we propose an interactive interface to explore 2D linear projections with visual patterns on subsets. First, we propose a voting-based algorithm to recommend optimal projection, in which the identified pattern looks the most salient. Specifically, we propose three kinds of point-wise quality metrics of 2D linear projections for outliers, clusterings, and trends, respectively. For each sampled projection, we measure its importance by accumulating the metrics of selected points. The projection with the highest importance is recommended. Second, we design an exploring interface with a scatterplot, a projection trail map, and a control panel. Our interface allows users to explore projections by specifying interested data subsets. At last, we employ three datasets and demonstrate the effectiveness of our approach through three case studies of exploring clusters, outliers, and trends.