EvoGraphDice: Interactive evolution for visual analytics

Abstract

Abstract-Visualization of large and complex datasets is a research challenge, especially in frameworks like industrial design, decision making and visual analytics. Interactive Evolution, used not only as an optimisation tool, but also as an exploration tool may provide some versatile solutions to this challenge. This paper presents an attempt in this direction with the EvoGraphDice prototype, developed on top of GraphDice, a general purpose visualization freeware for multidimensional visualization based on scatterplot matrices. EvoGraphDice interactively evolves compound additional dimensions, that provide new viewpoints on a multidimensional dataset. Compound dimensions are linear combination of the initial data dimensions, they are initialised with a Principal Component Analysis (PCA), and modified progressively by the interactive evolution process. Various interactions are available to the user, either in a transparent way, via a capture of mouse-clicks, or in a fully controlled manner, where the user has the opportunity to modify or include his own compound dimension in the evolved population, control the search space, or do some interactive queries. EvoGraphDice is tested on a synthetic dataset of dimension 6, where a known dependency is rediscovered via interactive manipulation. A second example is presented, based on a real dataset of dimension 13, provided by an industrial partner. Our experiments prove the potential of this interactive approach, and allow us to sketch future directions of development for the EvoGraphDice prototype.

VizAssist: an interactive user assistant for visual data mining

“…In [6], IGAs were used to detect outliers in the parallel coordinate visualization. In [7], a 2D scatter plot was used and the interactive evolution modified the linear combination of attributes. These studies show that the IGA principles can be useful for VDM.…”

Evolutionary visual exploration

“…a combined dimension) using the "dimension editor" in figure 1-j, or limit the dimension search space (figure 1-i), which results in a system reset similar to pressing the "restart" button. Note that many EA parameters can be tuned, such as the fitness threshold and crossover/mutation/replacement rates (see [4]). …”

“…A first version of EvoGraphDice [4] was based on an IEA that only manipulated linear combinations of dimensions. Recent extensions, described in [3], are (i) a Genetic Programming (GP) algorithm allowing the manipulation of nonlinear combinations of dimensions as variable size mathematical formulae, (ii) user assessment of proposed views is explicitly captured via a slider, (iii) a surrogate function based on some specific geometric measurements (scagnos- Figure 2: Nine scagnostics measures from [16].…”

“…Formalised as an optimization problem (e.g. Evolutionary Computation), interesting features are selected based on a fitness function [4].…”

“…EvoGraphDice [3,4] is an EVE prototype for the exploration of multidimensional datasets: the system proposes interesting views based on both objective measures, such as visual patterns in the two-dimensional projections of the data, and subjective measures corresponding to user satisfaction with the presented view. EvoGraphDice was built as an extension of an existing scatterplot matrix inspection tool.…”