High-Dimensional Data Visualization by Interactive Construction of Low-Dimensional Parallel Coordinate Plots

TL;DR

This paper introduces an interactive method for visualizing high-dimensional data by constructing low-dimensional parallel coordinate plots through user-guided sampling of correlated dimension subsets, enhancing pattern discovery.

Contribution

It presents a novel interactive technique that constructs focused low-dimensional PCPs from high-dimensional data using graph-based sampling of correlated dimensions.

Findings

Effective visualization of high-dimensional data patterns

Facilitates discovery of new data relationships

Improves interpretability of complex datasets

Abstract

Parallel coordinate plots (PCPs) are among the most useful techniques for the visualization and exploration of high-dimensional data spaces. They are especially useful for the representation of correlations among the dimensions, which identify relationships and interdependencies between variables. However, within these high-dimensional spaces, PCPs face difficulties in displaying the correlation between combinations of dimensions and generally require additional display space as the number of dimensions increases. In this paper, we present a new technique for high-dimensional data visualization in which a set of low-dimensional PCPs are interactively constructed by sampling user-selected subsets of the high-dimensional data space. In our technique, we first construct a graph visualization of sets of well-correlated dimensions. Users observe this graph and are able to interactively select the dimensions by sampling from its cliques, thereby dynamically specifying the most relevant lower dimensional data to be used for the construction of focused PCPs. Our interactive sampling overcomes the shortcomings of the PCPs by enabling the visualization of the most meaningful dimensions (i.e., the most relevant information) from high-dimensional spaces. We demonstrate the effectiveness of our technique through two case studies, where we show that the proposed interactive low-dimensional space constructions were pivotal for visualizing the high-dimensional data and discovering new patterns.