A Recursive Subdivision Technique for Sampling Multi-class Scatterplots

IEEE Trans Vis Comput Graph. 2020 Jan;26(1):729-738. doi: 10.1109/TVCG.2019.2934541. Epub 2019 Aug 22.

Abstract

We present a non-uniform recursive sampling technique for multi-class scatterplots, with the specific goal of faithfully presenting relative data and class densities, while preserving major outliers in the plots. Our technique is based on a customized binary kd-tree, in which leaf nodes are created by recursively subdividing the underlying multi-class density map. By backtracking, we merge leaf nodes until they encompass points of all classes for our subsequently applied outlier-aware multi-class sampling strategy. A quantitative evaluation shows that our approach can better preserve outliers and at the same time relative densities in multi-class scatterplots compared to the previous approaches, several case studies demonstrate the effectiveness of our approach in exploring complex and real world data.

Publication types

  • Research Support, Non-U.S. Gov't