2015 IEEE Pacific Visualization Symposium (PacificVis) (2015)
April 14, 2015 to April 17, 2015
Fangfang Zhou , Central South University, China
Wei Huang , Central South University, China
Juncai Li , Central South University, China
Yezi Huang , Central South University, China
Yang Shi , Central South University, China
Ying Zhao , Central South University, China
Radviz is a radial visualization technique which maps data from multiple dimensional space onto a planar picture. The dimensions placed on the circumference of a circle, called Dimension Anchors (DAs), can be reordered to reveal different patterns in the dataset. Extending the number of dimensions can enhance the flexibility in the placement of the DAs to explore more meaningful visualizations. In this paper, we describe a method which rationally extends a dimension to multiple new dimensions in Radviz. This method first calculates the probability distribution histogram of a dimension. The mean shift algorithm is applied to get centers of probability density to segment the histogram, and then the dimension can be extended according to the number of segments of the histogram. We also suggest using the Dunn's index to find the optimal placement of DAs, so the better effect of visual clustering could be achieved after the dimension expansion in Radviz. Finally, we demonstrate the usability of our approach on visually analysing the iris data and two other datasets.
Data visualization, Histograms, Probability distribution, Iris, Visualization, Bandwidth, Indexes
F. Zhou, Wei Huang, Juncai Li, Yezi Huang, Yang Shi and Ying Zhao, "Extending Dimensions in Radviz based on mean shift," 2015 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Hangzhou, China, 2015, pp. 111-115.