Parallel Sets: interactive exploration and visual analysis of categorical data

Abstract

Categorical data dimensions appear in many real-world data sets, but few visualization methods exist that properly deal with them. Parallel Sets are a new method for the visualization and interactive exploration of categorical data that shows data frequencies instead of the individual data points. The method is based on the axis layout of parallel coordinates, with boxes representing the categories and parallelograms between the axes showing the relations between categories. In addition to the visual representation, we designed a rich set of interactions. Parallel Sets allow the user to interactively remap the data to new categorizations and, thus, to consider more data dimensions during exploration and analysis than usually possible. At the same time, a metalevel, semantic representation of the data is built. Common procedures, like building the cross product of two or more dimensions, can be performed automatically, thus complementing the interactive visualization. We demonstrate Parallel Sets by analyzing a large CRM data set, as well as investigating housing data from two US states.

Decision Exploration Lab: A Visual Analytics Solution for Decision Management

“…This question is of a higher level than what typical software comprehension tools, such as dependency graphs and executions charts, directly address. [32], and the contingency wheel [3]. Although these methods are specifically designed for categorical data, they are also more focused on, and effective for, frequency-related tasks.…”

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

“…Parallel Sets by Kosara et al [58] is a successful example where the overlaps between groups is presented with a limited amount of interaction. In the software visualization domain, Telea and Auber [59] represent the changes in code structures using a flow layout where they identify steady code blocks and when splits occur in the code of a software.…”

Visual Search and Analysis in Complex Information Spaces—Approaches and Research Challenges

“…In [12] we propose an approach that addresses the analysis of grouped spatio-temporal data. It is based on the notion of Parallel Sets [25], extended for automatic identification of interesting points in time that are suggested to the user for inspection.…”